Reviewerly/exHarmony huggingface dataset:
Development of Pronunciation Textbooks Based on Communicative Language Teaching Approach

Research has found out that speaking skill covered pronunciation as the fundamental significance in linguistic proficiency. Surprisingly, in most English Language test, pronunciation appears to be the subject that is often neglected. Despite the generous amount of media utilize to practice utterance of English syllable or words, the supply of pronunciation teaching textbook source is scarce. By employing research and development (R&amp;D) of 4D design, this study aimed at describing the model, validity and reliability of the Pronunciation textbook for English Education students. This research involved 38 students and 2 (two) experts’ rater to examine the book based on BSNP (Badan Standar Nasional Pendidikan or National Education Standard Agency) criteria. The result pointed out, that the model of pronunciation book corresponded with the KKNI standard curriculum and based on BSNP Criteria. The questionnaires validation result from respondent showed that rxy is 0.64 with the category of valid, and the reliability test result from Cronbach Alfa showed that r11 is 1.04 in the level of ?1.00 with the category of very high reliability. The validity and reliability of the product were also verified by two experts’ rater and analyzed by using Aiken index technique. In this analysis, the V index showed the value of 0.91 with the category of very high (from the scale of 0.8-1.00) for the content eligibility, presentation, language and graphic. Accordingly, Pronunciation textbooks model developed based on communicative language teaching approach is valid and reliable.
SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision

The development and application of modern technology are an essential basis for the efficient monitoring of species in natural habitats to assess the change of ecosystems, species communities and populations, and in order to understand important drivers of change. For estimating wildlife abundance, camera trapping in combination with three-dimensional (3D) measurements of habitats is highly valuable. Additionally, 3D information improves the accuracy of wildlife detection using camera trapping. This study presents a novel approach to 3D camera trapping featuring highly optimized hardware and software. This approach employs stereo vision to infer the 3D information of natural habitats and is designated as StereO CameRA Trap for monitoring of biodivErSity (SOCRATES). A comprehensive evaluation of SOCRATES shows not only a 3.23% improvement in animal detection (bounding box mAP75), but also its superior applicability for estimating animal abundance using camera trap distance sampling. The software and documentation of SOCRATES is openly provided.
The relationship between mobile broadband usage and user mobility with lockdown restrictions in Spain

This paper studies how COVID-19 lockdown restrictions encouraged and allowed people to form habits of increased mobile phone usage in Spain, even after the most restrictive measures were lifted. We have used data from the mobile network of a national telecommunications operator to study the influence of 15 different mobility restrictions on citizens’ use of mobile phones from 15 March 2020 to 15 April 2021 in a big Spanish city. In order to approach this issue, a quasi-experimental technique (before and after methodology) was implemented. We found that the use of smartphones presented an initial reduction with the first and most drastic mobility restrictions, but showed a growing use after their removal. However, user mobility was negatively influenced by all the mobility restrictions.
Forgery Analysis Method for Audio Recordings Generated by Voice Recorder Application from Samsung Smartphones

Audio recordings are used widely as crucial evidence due to the popularity of smartphones. However, the digital evidence submitted voluntarily may be questioned for its authentication. Since most smartphones provide the function of digital editing files, they could be exposed to crime as being edited maliciously. Primarily, it could be severe in the case of voluntary submission via information and communications networks. This paper proposed the forensic method for authentication of audio recordings generated by the Voice Recorder application from Samsung smartphones. First, we analyzed and compared the file structures and timestamps of audio recordings between the normal state without manipulation and the manipulated state. Then, we analyzed the spectrogram of audio recordings. Finally, by analyzing the timestamps of the media-log for audio recordings, we confirmed the authentication method for the audio recordings forensically.
Estimating the randomness of quantum circuit ensembles up to 50 qubits

Abstract Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole information. The ability of random circuits to approximate any random unitaries has consequences on their complexity, expressibility, and trainability. To study this property of random circuits, we develop numerical protocols for estimating the frame potential, the distance between a given ensemble and the exact randomness. Our tensor-network-based algorithm has polynomial complexity for shallow circuits and is high-performing using CPU and GPU parallelism. We study 1. local and parallel random circuits to verify the linear growth in complexity as stated by the Brown–Susskind conjecture, and; 2. hardware-efficient ansätze to shed light on its expressibility and the barren plateau problem in the context of variational algorithms. Our work shows that large-scale tensor network simulations could provide important hints toward open problems in quantum information science.
RETHINKING VISUAL ARTS EDUCATION WITH NEW TECHNOLOGIES AND RESOURCES DURING THE COVID PANDEMIC

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 782-791ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0246Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images

Semantic segmentation of biomedical images found its niche in screening and diagnostic applications. Recent methods based on deep learning convolutional neural networks have been very effective, since they are readily adaptive to biomedical applications and outperform other competitive segmentation methods. Inspired by the U-Net, we designed a deep learning network with an innovative architecture, hereafter referred to as AID-U-Net. Our network consists of direct contracting and expansive paths, as well as a distinguishing feature of containing sub-contracting and sub-expansive paths. The implementation results on seven totally different databases of medical images demonstrated that our proposed network outperforms the state-of-the-art solutions with no specific pre-trained backbones for both 2D and 3D biomedical image segmentation tasks. Furthermore, we showed that AID-U-Net dramatically reduces time inference and computational complexity in terms of the number of learnable parameters. The results further show that the proposed AID-U-Net can segment different medical objects, achieving an improved 2D F1-score and 3D mean BF-score of 3.82% and 2.99%, respectively.
Likelihood ratio to evaluate handwriting evidence using similarity index

Abstract Previous methods to evaluate evidence from handwriting examinations were usually associated with a redefinition of how these examinations are to be made. Here we propose the likelihood ratio method for handwriting evidence evaluation which is fully compatible with the current handwriting examination protocols. The method is focused on the similarity between handwriting samples, quantified using Jaccard index from results of a usual forensic handwriting comparison. The numerator of the likelihood ratio is the probability of a given class of similarity, assuming that a given person wrote the questioned sample. The denominator is the probability of the same class of similarity, assuming that a randomly selected person wrote questioned sample. The similarity distribution to quantify the numerator is derived from comparisons across reference handwritings. To calculate the denominator we propose to develop similarity distributions relevant for particular forensic scenarios. In the proof-of-a-concept study, we developed the distribution for the simulation scenario.
Generalized Zero-Shot Learning for Image Classification—Comparing Performance of Popular Approaches

There are many areas where conventional supervised machine learning does not work well, for instance, in cases with a large, or systematically increasing, number of countably infinite classes. Zero-shot learning has been proposed to address this. In generalized settings, the zero-shot learning problem represents real-world applications where test instances are present during inference. Separately, recently, there has been increasing interest in meta-classifiers, which combine the results from individual classifications to improve the overall classification quality. In this context, the purpose of the present paper is two-fold: First, the performance of five state-of-the-art, generalized zero-shot learning methods is compared for five popular benchmark datasets. Second, six standard meta-classification approaches are tested by experiment. In the experiments undertaken, all meta-classifiers were applied to the same datasets; their performance was compared to each other and to the original classifiers.
Fostering trust with transparency in the data economy era

Why is it hard for online users to trust service providers when it comes to their personal data? While users might give away their data when using their services, this does not mean that they necessarily trust these companies. Building trust in online services is particularly relevant as digital economy policy strategies, such as the EU Data Strategy, deposit a considerable amount of faith in the benefits of a data-driven society. To achieve this goal, transparency should be considered a necessary feature, on which trust can be built. According to scholarly literature, the more information provided to data subjects, the less power asymmetry, caused by a lack of knowledge, between them and data controllers will exist. In this respect, transparency around data processing has been, and still is, conveyed through privacy notices. But these are far from being used as helpful tools to navigate complex data-intensive environments. Technical developments, such as Solid personal datastores, provide a fertile ground for the negotiation of privacy terms between the involved parties. But to do so, it is necessary to have clear and transparent processing conditions. However, while certain specifications have been developed to accommodate for the representation of privacy terms, there is still a lack of developed solutions to address this problem. With this in mind, we propose the usage of the Privacy Paradigm ODRL Profile (PPOP), which extends ODRL and DPV to specify data processing requirements for personal datastores envisaged as key core elements of the data economy. To demonstrate the usage of PPOP, a set of policy examples will be provided, as well as a prototype implementation ofa generator of machine and human-readable PPOP policies.
A construction of combinatorial NLTS

The NLTS (No Low-Energy Trivial State) conjecture [M. H. Freedman and M. B. Hastings, Quantum Inf. Comput. 14, 144 (2014)] posits that there exist families of Hamiltonians with all low energy states of high complexity (with complexity measured by the quantum circuit depth preparing the state). Here, we prove a weaker version called the combinatorial no low error trivial states (NLETS), where a quantum circuit lower bound is shown against states that violate a (small) constant fraction of local terms. This generalizes the prior NLETS results [L. Eldar and A. W. Harrow, in 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS) (IEEE, 2017), pp. 427–438] and [Nirkhe et al., in 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018), Leibniz International Proceedings in Informatics (LIPIcs), edited by Chatzigiannakis et al. (Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, 2018), Vol. 107, pp. 1–11]. Our construction is obtained by combining tensor networks with expander codes [M. Sipser and D. Spielman, IEEE Trans. Inf. Theory 42, 1710 (1996)]. The Hamiltonian is the parent Hamiltonian of a perturbed tensor network, inspired by the “uncle Hamiltonian” of Fernández-González et al. [Commun. Math. Phys. 333, 299 (2015)]. Thus, we deviate from the quantum Calderbank-Shor-Steane (CSS) code Hamiltonians considered in most prior works.
A Deep-Learning-Based Approach for Saliency Determination on Point Clouds

Laser scanners recording a huge number of data points from different surfaces are widely used to capture the exact geometry of 3D objects. These large amounts of data require intelligent solutions to be examined and processed efficiently. Deep-learning-based approaches have found their way into many data analytics applications for processing such large datasets, categorizing them, or even determining the most informative portion of the data. This research focused on 3D deep-learning techniques directly applied to point clouds to determine the most important features of a 3D shape. More specifically, this research adopted PointNet as a backbone architecture for feature extraction from 3D point clouds and computed a gradient-based class activation mapping (Grad-CAM) on each object to create a 3D importance/saliency map. Experiments confirmed the success of the proposed approach in the determination of important features of 3D objects as compared with the ground truth.
FORMATION OF INFROMATION COMPETENCES OF PUPILS OF GENERAL SECONDARY EDUCATION INSTITUTIONS OF THE FIRST DEGREE BY MEANS OF GOOGLE SERVICES APPLYING

The article analyzes the problem of information competence’s formation of pupils of general secondary education institutions of the first degree applying the Google services. It was noted that today the issue of information competence’s formation of pupils, has been actualized, especially in conditions of distance learning. The main purpose of the article is to identify the possibilities of using Google services in the educational process of general secondary education of the first degree, to establish and describe prospects of formation of information competence’s formation of pupils of general secondary education institutions of the first degree. То achieve the goal and objectives of the research, analytical and synthetic methods of working with scientific and methodical sources, analysis of own experience in the field of IT technologies were used to determine the toolkit of Google services for the formation of innovative competence of children of primary school age The content of the following concepts: «competence», «information competence», «digital competence», «information service» and «modern interactive educational environment» was analyzed and disclosed in this article. The concept of information competence and its formation according to the requirements of the new Ukrainian school was considered. The particular attention was focused on the effective usage of information technologies during epidemiological and military events. The list of the best information services that can be applied in the education process in order to form the information competence of primary students is presented. Detailed instructions for all information services considered in this article were developed. These instructions were presented with a brief description of work and effective usage in the educational process of distance learning. In addition, the patterns of structuring the corresponding services in the form of interactive educational environment were described. The format of the offered interactive educational environments meets all the standards and requirements provided by the Ministry of Education and Science of Ukraine.
HCMS: Hierarchical and Conditional Modality Selection for Efficient Video Recognition

Videos are multimodal in nature. Conventional video recognition pipelines typically fuse multimodal features for improved performance. However, this is not only computationally expensive but also neglects the fact that different videos rely on different modalities for predictions. This paper introduces Hierarchical and Conditional Modality Selection (HCMS), a simple yet efficient multimodal learning framework for efficient video recognition. HCMS operates on a low-cost modality, i.e., audio clues, by default, and dynamically decides on-the-fly whether to use computationally-expensive modalities, including appearance and motion clues, on a per-input basis. This is achieved by the collaboration of three LSTMs that are organized in a hierarchical manner. In particular, LSTMs that operate on high-cost modalities contain a gating module, which takes as inputs lower-level features and historical information to adaptively determine whether to activate its corresponding modality; otherwise it simply reuses historical information. We conduct extensive experiments on two large-scale video benchmarks, FCVID and ActivityNet, and the results demonstrate the proposed approach can effectively explore multimodal information for improved classification performance while requiring much less computation.
Blockchain-Based Smart Energy Communities: Operation of Smart Legal Contract

The Blockchain paradigm applied to energy communities represents both a challenge and an opportunity. Indeed, it introduces a new framework to manage the communities’ services and, in the meantime, guarantees the traceability of the energy produced and consumed, informing consumers of the origins and costs of their supply, making tariffs more transparent with the result of promoting energy flexibility. Blockchain enables a real digitalization of the energy sector. The security of the transaction and the guarantee that the platform on which an energy network is based cannot be tampered with has prompted energy communities to start a blockchain-based system. The paper proposed a blockchain-based open and transparent ecosystem for smart energy communities within, different and several actors interfacing thanks to blockchain system and its services interact in a natural and disintermediate way. It allows, in a peer to peer (p2p) way, to implement participatory logic aimed at reducing and self-consumption of energy and direct participation citizens to the electricity system. A proof of concept is created and tested using the Trakti blockchain platform (scalable) that, through an end-to-end approach, implement a new dynamic smart legal contract with real data for the local marketplace within a smart energy community. The results show that the new smart Legal Contract makes it possible to define an energy token, which allows remunerating the energy exchanges in communities between producers and consumers within a smart grid and converting the energy into community services.
Witnessing non-Markovianity by quantum quasi-probability distributions

Abstract We employ frames consisting of rank-one projectors (i.e. pure quantum states) and their induced informationally complete quantum measurements to represent generally mixed quantum states by quasi-probability distributions. In the case of discrete frames on finite dimensional systems this results in a vector like representation by quasi-probability vectors, while for the continuous frame of coherent states in continuous variable (CV) systems the approach directly leads to the celebrated representation by Glauber–Sudarshan P-functions and Husimi Q-functions. We explain that the Kolmogorov distances between these quasi-probability distributions lead to upper and lower bounds of the trace distance which measures the distinguishability of quantum states. We apply these results to the dynamics of open quantum systems and construct a non-Markovianity witness based on the Kolmogorov distance of the P- and Q-functions. By means of several examples we discuss the performance of this witness and demonstrate that it is useful in the regime of high entropy states for which a direct evaluation of the trace distance is typically very demanding. For Gaussian dynamics in CV systems we even find a suitable non-Markovianity measure based on the Kolmogorov distance between the P-functions which can alternatively be used as a witness for non-Gaussianity.
Improving diversity and quality of adversarial examples in adversarial transformation network

This paper proposes PatternAttack to mitigate two major issues of Adversarial Transformation Network (ATN) including the low diversity and the low quality of adversarial examples. In order to deal with the first issue, this research proposes a stacked convolutional autoencoder based on patterns to generalize ATN. This proposed autoencoder could support different patterns such as all-pixel pattern, object boundary pattern, and class model map pattern. In order to deal with the second issue, this paper presents an algorithm to improve the quality of adversarial examples in terms of $$L_0$$ -norm and $$L_2$$ -norm. This algorithm employs adversarial pixel ranking heuristics such as JSMA and COI to prioritize adversarial pixels. To demonstrate the advantages of the proposed method, comprehensive experiments have been conducted on the MNIST dataset and the CIFAR-10 dataset. For the first issue, the proposed autoencoder generates diverse adversarial examples. For the second issue, the proposed algorithm significantly improves the quality of adversarial examples. In terms of $$L_0$$ -norm, the proposed algorithm decreases from hundreds of adversarial pixels to one adversarial pixel. In terms of $$L_2$$ -norm, the proposed algorithm reduces the average distance considerably. These results show that the proposed method can generate high-quality and diverse adversarial examples in practice.
Computable lower bounds on the entanglement cost of quantum channels

Abstract A class of lower bounds for the entanglement cost of any quantum state was recently introduced in Lami and Regula (2023 Nature Physics ) in the form of entanglement monotones known as the tempered robustness and tempered negativity. Here we extend their definitions to point-to-point quantum channels, establishing a lower bound for the asymptotic entanglement cost of any channel, whether finite or infinite dimensional. This leads, in particular, to a bound that is computable as a semidefinite program and that can outperform previously known lower bounds, including ones based on quantum relative entropy. In the course of our proof we establish a useful link between the robustness of entanglement of quantum states and quantum channels, which requires several technical developments such as showing the lower semicontinuity of the robustness of entanglement of a channel in the weak*-operator topology on bounded linear maps between spaces of trace class operators.