Reviewerly/exHarmony huggingface dataset:
Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language

In recent years, the use of social networks has increased exponentially, which has led to a significant increase in cyberbullying. Currently, in the field of Computer Science, research has been made on how to detect aggressiveness in texts, which is a prelude to detecting cyberbullying. In this field, the main work has been done for English language texts, mainly using Machine Learning (ML) approaches, Lexicon approaches to a lesser extent, and very few works using hybrid approaches. In these, Lexicons and Machine Learning algorithms are used, such as counting the number of bad words in a sentence using a Lexicon of bad words, which serves as an input feature for classification algorithms. This research aims at contributing towards detecting aggressiveness in Spanish language texts by creating different models that combine the Lexicons and ML approach. Twenty-two models that combine techniques and algorithms from both approaches are proposed, and for their application, certain hyperparameters are adjusted in the training datasets of the corpora, to obtain the best results in the test datasets. Three Spanish language corpora are used in the evaluation: Chilean, Mexican, and Chilean-Mexican corpora. The results indicate that hybrid models obtain the best results in the 3 corpora, over implemented models that do not use Lexicons. This shows that by mixing approaches, aggressiveness detection improves. Finally, a web application is developed that gives applicability to each model by classifying tweets, allowing evaluating the performance of models with external corpus and receiving feedback on the prediction of each one for future research. In addition, an API is available that can be integrated into technological tools for parental control, online plugins for writing analysis in social networks, and educational tools, among others.
UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring

The visualization and detection of anomalies (outliers) are of crucial importance to many fields, particularly cybersecurity. Several approaches have been proposed in these fields, yet to the best of our knowledge, none of them has fulfilled both objectives, simultaneously or cooperatively, in one coherent framework. The visualization methods of these approaches were introduced for explaining the output of a detection algorithm, not for data exploration that facilitates a standalone visual detection. This is our point of departure: UN-AVOIDS, an unsupervised and nonparametric approach for both visualization (a human process) and detection (an algorithmic process) of outliers, that assigns invariant anomalous scores (normalized to $[0,1]$), rather than hard binary-decision. The main aspect of novelty of UN-AVOIDS is that it transforms data into a new space, which is introduced in this paper as neighborhood cumulative density function (NCDF), in which both visualization and detection are carried out. In this space, outliers are remarkably visually distinguishable, and therefore the anomaly scores assigned by the detection algorithm achieved a high area under the ROC curve (AUC). We assessed UN-AVOIDS on both simulated and two recently published cybersecurity datasets, and compared it to three of the most successful anomaly detection methods: LOF, IF, and FABOD. In terms of AUC, UN-AVOIDS was almost an overall winner. The article concludes by providing a preview of new theoretical and practical avenues for UN-AVOIDS. Among them is designing a visualization aided anomaly detection (VAAD), a type of software that aids analysts by providing UN-AVOIDS' detection algorithm (running in a back engine), NCDF visualization space (rendered to plots), along with other conventional methods of visualization in the original feature space, all of which are linked in one interactive environment.
Simulation platform for pattern recognition based on reservoir computing with memristor networks

Memristive systems and devices are potentially available for implementing reservoir computing (RC) systems applied to pattern recognition. However, the computational ability of memristive RC systems depends on intertwined factors such as system architectures and physical properties of memristive elements, which complicates identifying the key factor for system performance. Here we develop a simulation platform for RC with memristor device networks, which enables testing different system designs for performance improvement. Numerical simulations show that the memristor-network-based RC systems can yield high computational performance comparable to that of state-of-the-art methods in three time series classification tasks. We demonstrate that the excellent and robust computation under device-to-device variability can be achieved by appropriately setting network structures, nonlinearity of memristors, and pre/post-processing, which increases the potential for reliable computation with unreliable component devices. Our results contribute to an establishment of a design guide for memristive reservoirs toward a realization of energy-efficient machine learning hardware.
Optimal universal quantum error correction via bounded reference frames

Error correcting codes with a universal set of transversal gates are a desideratum for quantum computing. Such codes, however, are ruled out by the Eastin-Knill theorem. Moreover, the theorem also rules out codes which are covariant with respect to the action of transversal unitary operations forming continuous symmetries. In this work, starting from an arbitrary code, we construct approximate codes which are covariant with respect to the entire group of local unitary gates in dimension $d\phantom{\rule{4pt}{0ex}}(<\ensuremath{\infty})$, using quantum reference frames. We show that our codes are capable of efficiently correcting different types of erasure errors. When only a small fraction of the $n$ qudits upon which the code is built are erased, our covariant code has an error that scales as $1/{n}^{2}$, which is reminiscent of the Heisenberg limit of quantum metrology. When every qudit has a chance of being erased, our covariant code has an error that scales as $1/n$. We show that the error scaling is optimal in both cases. Our approach has implications for fault-tolerant quantum computing, reference frame error correction, and the AdS-CFT duality.
Squeezing by critical speeding up: Applications in quantum metrology

We present an alternative protocol allowing for the preparation of critical states that instead of suffering from the critical slowing-down benefits from the critical speeding-up. Paradoxically, we prepare these states by going away from the critical point which allows for the speed-up. We apply the protocol to the paradigmatic quantum Rabi model and its classical oscillator limit as well as the Lipkin-Meshkov-Glick model. Subsequently, we discuss the application of the adiabatic speed-up protocol in quantum metrology and compare its performance with critical quantum metrology. We show that critical quantum metrology with the Lipkin-Meshkov-Glick model cannot even overcome the standard quantum limit, and we argue that, even though critical metrology protocols can overcome it in some cases, critical metrology is a suboptimal metrological strategy. Finally, we conclude that systems exhibiting a phase transition are indeed interesting from the viewpoint of quantum technologies, however, it may not be the critical point that should attract the most attention.
Error-correcting entanglement swapping using a practical logical photon encoding

Several emerging quantum technologies, including quantum networks, modular and fusion-based quantum computing, rely crucially on the ability to perform photonic Bell state measurements. Therefore, photon losses and the 50\% success probablity upper bound of Bell state measurements pose a critical limitation to photonic quantum technologies. Here, we develop protocols that overcome these two key challenges through logical encoding of photonic qubits. Our approach uses a tree graph state logical encoding, which can be produced deterministically with a few quantum emitters, and achieves near-deterministic logical photonic Bell state measurements while also protecting against errors including photon losses, with a record loss-tolerance threshold.
Feature Fusion of a Deep-Learning Algorithm into Wearable Sensor Devices for Human Activity Recognition

This paper presents a wearable device, fitted on the waist of a participant that recognizes six activities of daily living (walking, walking upstairs, walking downstairs, sitting, standing, and laying) through a deep-learning algorithm, human activity recognition (HAR). The wearable device comprises a single-board computer (SBC) and six-axis sensors. The deep-learning algorithm employs three parallel convolutional neural networks for local feature extraction and for subsequent concatenation to establish feature fusion models of varying kernel size. By using kernels of different sizes, relevant local features of varying lengths were identified, thereby increasing the accuracy of human activity recognition. Regarding experimental data, the database of University of California, Irvine (UCI) and self-recorded data were used separately. The self-recorded data were obtained by having 21 participants wear the device on their waist and perform six common activities in the laboratory. These data were used to verify the proposed deep-learning algorithm on the performance of the wearable device. The accuracy of these six activities in the UCI dataset and in the self-recorded data were 97.49% and 96.27%, respectively. The accuracies in tenfold cross-validation were 99.56% and 97.46%, respectively. The experimental results have successfully verified the proposed convolutional neural network (CNN) architecture, which can be used in rehabilitation assessment for people unable to exercise vigorously.
Efficient Full Higher-Order Unification

We developed a procedure to enumerate complete sets of higher-order unifiers based on work by Jensen and Pietrzykowski. Our procedure removes many redundant unifiers by carefully restricting the search space and tightly integrating decision procedures for fragments that admit a finite complete set of unifiers. We identify a new such fragment and describe a procedure for computing its unifiers. Our unification procedure, together with new higher-order term indexing data structures, is implemented in the Zipperposition theorem prover. Experimental evaluation shows a clear advantage over Jensen and Pietrzykowski's procedure.
Improvement method of high-temperature digital image correlation measurement accuracy based on image processing

With the increasing application of digital image correlation (DIC) technology in the field of high-temperature measurement, thermal radiation and disturbance in high-temperature environment restrict the development of high-temperature DIC technology. In this study, we propose a method based on image processing to improve the measuring accuracy of DIC technique of high temperature for solving the above mentioned problems. Firstly, the image dehazing enhancement processing and the gray scale range stretching processing are used to eliminate the influence of the thermal radiation background light to enhance the image contrast. Then, the inverse filtering image recovery processing is used to eliminate the effect of the thermal perturbation. Lastly, a series of high-temperature image acquisition and thermal disturbance elimination experiments at 1200 °C are conducted. The image collected by the camera is enhanced and restored according to the above mentioned image processing method. Experimental results show that this method can acquire high-contrast clear images under high-temperature conditions, effectively eliminate the interference of thermal disturbance noise in high-temperature measurement and improve the measurement accuracy.
Human-State-Aware Controller for a Tethered Aerial Robot Guiding a Human by Physical Interaction

With the rapid development of Aerial Physical Interaction, the possibility to have aerial robots physically interacting with humans is attracting a growing interest. In one of our previous works, we considered one of the first systems in which a human is physically connected to an aerial vehicle by a cable. There, we developed a compliant controller that allows the robot to pull the human toward a desired position using forces only as an indirect communication-channel. However, this controller is based on the robot-state only, which makes the system not adaptable to the human behavior, and in particular to their walking speed. This reduces the effectiveness and comfort of the guidance when the human is still far from the desired point. In this paper, we formally analyze the problem and propose a human-state-aware controller that includes a human`s velocity feedback. We theoretically prove and experimentally show that this method provides a more consistent guiding force which enhances the guiding experience.
Features of Technologies for Transmission of Radio and Television in 4G / 5G Networks

Modern mobile networks have long since turned from telephone to multiservice networks, and the most dynamically developing area of services is video transmission. This is the category of traffic, the share of which in cellular networks is growing steadily every year. If in 2012 it accounted for half of the traffic, in 2016 – 60%, now it is about 70%, and by 2021 the share of video is expected to grow to 78%. And all this against the backdrop of a fivefold increase in the total volume of mobile traffic over the past years. Therefore, in the development of high-speed mobile communication standards, considerable attention is paid to video and television transmission technologies.
Noise analysis of electrical circuits on fractal set

Purpose The purpose of this study is to originally present noise analysis of electrical circuits defined on fractal set. Design/methodology/approach The fractal integrodifferential equations of resistor-inductor, resistor-capacitor, inductor-capacitor and resistor-inductor-capacitor circuits subjected to zero mean additive white Gaussian noise defined on fractal set have been formulated. The fractal time component has also been considered. The closed form expressions for crucial stochastic parameters of circuit responses have been derived from these equations. Numerical simulations of power spectral densities based on the derived autocorrelation functions have been performed. A comparison with those without fractal time component has been made. Findings We have found that the Hausdorff dimension of the middle b Cantor set strongly affects the power spectral densities; thus, the average powers of noise induced circuit responses and the inclusion of fractal time component causes significantly different analysis results besides the physical measurability of electrical quantities. Originality/value For the first time, the noise analysis of electrical circuit on fractal set has been performed. This is also the very first time that the fractal time component has been included in the fractal calculus-based circuit analysis.
Sim2Air - Synthetic Aerial Dataset for UAV Monitoring

In this letter, we propose a novel approach to generate a synthetic aerial dataset for application in UAV monitoring. We propose to accentuate shape-based object representation by applying texture randomization. A diverse dataset with photorealism in all parameters such as shape, pose, lighting, scale, viewpoint, etc. except for atypical textures is created in a 3D modelling software Blender. Our approach specifically targets two conditions in aerial images where texture of objects is difficult to detect, namely challenging illumination and objects occupying only a small portion of the image. Experimental evaluation of YOLO and Faster R-CNN detectors trained on synthetic data with randomized textures confirmed our approach by increasing the mAP value (17 and 3.7 percentage points for YOLO; 20 and 1.1 percentage points for Faster R-CNN) on two test datasets of real images, both containing UAV-to-UAV images with motion blur. Testing on different domains, we conclude that the more the generalisation ability is put to the test, the more apparent are the advantages of the shape-based representation.
Neural-network-based qubit-environment characterization

The exact microscopic structure of the environments that produces $1/f$ noise in superconducting qubits remains largely unknown, hindering our ability to have robust simulations and harness the noise. In this paper we show how it is possible to infer information about such an environment based on a single measurement of the qubit coherence, circumventing any need for separate spectroscopy experiments. Similarly to other spectroscopic techniques, the qubit is used as a probe which interacts with its environment. The complexity of the relationship between the observed qubit dynamics and the impurities in the environment makes this problem ideal for machine learning methods - more specifically neural networks. With our algorithm we are able to reconstruct the parameters of the most prominent impurities in the environment, as well as differentiate between different environment models, paving the way towards a better understanding of $1/f$ noise in superconducting circuits.
Understanding software architecture erosion: A systematic mapping study

Architecture erosion (AEr) can adversely affect software development and has received significant attention in the last decade. However, there is an absence of a comprehensive understanding of the state of research about the reasons and consequences of AEr, and the countermeasures to address AEr. This work aims at systematically investigating, identifying, and analyzing the reasons, consequences, and ways of detecting and handling AEr. With 73 studies included, the main results are as follows: (1) AEr manifests not only through architectural violations and structural issues but also causing problems in software quality and during software evolution; (2) non-technical reasons that cause AEr should receive the same attention as technical reasons, and practitioners should raise awareness of the grave consequences of AEr, thereby taking actions to tackle AEr-related issues; (3) a spectrum of approaches, tools, and measures has been proposed and employed to detect and tackle AEr; and (4) three categories of difficulties and five categories of lessons learned on tackling AEr were identified. The results can provide researchers a comprehensive understanding of AEr and help practitioners handle AEr and improve the sustainability of their architecture. More empirical studies are required to investigate the practices of detecting and addressing AEr in industrial settings.
Neurophysiological responses to robot–human interactions in retail stores

Purpose Retailers are increasingly considering the introduction of service robots in their stores to support or even replace service staff. Service robots can execute service scripts during the service encounter that can influence customer interactions and the overall experience. While the role of service agents is well documented, more research is needed to understand customer responses to a technology-infused servicescape and to investigate the value of service robots as interaction partners. The purpose of this study is to examine the degree of customer immersion in human-human or human-robot interactions across different stages of the service experience and to understand how immersion affects store visit duration under each condition. Design/methodology/approach An experimental field study was developed to test the research hypotheses. The study was conducted in a retail store selling premium Italian leather goods with 50 respondents randomly allocated to one of two experimental conditions, interaction with a service robot or interaction with a human sales associate. Participants’ biometrics were collected to measure their immersion in the service encounter and to assess its impact on store visit duration. Findings The interaction with a service robot increases the level of customer immersion during the service encounter’s welcome and surprise moments. Immersion positively affects visit duration. However, participants exposed to a robot sales associate reported a shorter visit duration as compared to those who interacted with a human sales associate. Originality/value This study contributes to the emerging service and retail marketing literature on service robot applications applying a neuroscientific approach to the study of human–robot interactions across different moments of the service encounter. For managers, this study shows the conditions under which service robots can be successfully implemented in retail stores in accordance with the type of task performed and the degree of immersion generated in customers.
Standard quantum annealing outperforms adiabatic reverse annealing with decoherence

We study adiabatic reverse annealing (ARA) in an open system. In the closed system (unitary) setting, this annealing protocol allows avoidance of first-order quantum phase transitions of selected models, resulting in an exponential speedup compared with standard quantum annealing, provided that the initial state of the algorithm is close in Hamming distance to the target one. Here, we show that decoherence can significantly modify this conclusion: by resorting to the adiabatic master equation approach, we simulate the dynamics of the ferromagnetic $p$-spin model with $p=3$ under independent and collective dephasing. For both models of decoherence, we show that the performance of open system ARA is far less sensitive to the choice of the initial state than its unitary counterpart, and, most significantly, that open system ARA by and large loses its time to solution advantage compared to standard quantum annealing. These results suggest that as a stand-alone strategy, ARA is unlikely to experimentally outperform standard "forward" quantum annealing, and that error mitigation strategies will likely be required in order to realize the benefits of ARA in realistic, noisy settings.
Short-wavelength reverberant wave systems for physical realization of reservoir computing

Machine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures whose physical implementations promise compactness, high-speed execution, physical robustness, and low-energy cost. Here, we experimentally demonstrate an approach that uses the high sensitivity of reverberant short-wavelength waves for physical realization and enhancement of computational power of a type of ML known as reservoir computing (RC). The potential computation power of RC systems increases with their effective size. We here exploit the intrinsic property of short-wavelength reverberant wave sensitivity to perturbations to expand the effective size of the RC system by means of spatial and spectral perturbations. Working in the microwave regime, this scheme is tested experimentally on different ML tasks. Our results indicate the general applicability of reverberant wave based implementations of RC and of our effective reservoir size expansion techniques.