Reviewerly/exHarmony huggingface dataset:
Adaptively and spatially constrained dual-level trimap generation from sparse inputs

Trimap generation is currently playing an important role in the field of image matting. The current trimap generation methods cannot adaptively adjust the classification thresholds, and ignore the nonlocal correlations between pixels. Besides, they also need finely provided trimaps. This paper raises a dual-level trimap generation approach which requires sparse user inputs. The first level introduces a unary data term from adaptive learned thresholds combined with local spatial constraints to effectively overcome the drawback of fixed threshold methods. The second level introduces a smoothness term from a nonlocal affinity for foreground probability estimation, and a pixel correlation constraint is computed and added to the data term. The foreground probability is finally solved in a closed form solution and truncated to generate the final trimap. In the experiments, firstly, this approach raises a novel trimap evaluation criterion according to the two classic terms of missing and false classifications. Based on this criterion, the final trimaps generated by our approach are better than those generated by other methods. Secondly, in the final matting process from four typical matting algorithms, the matting results from the trimaps by our approach are still better than those from the trimaps by other trimap generation methods.
Unconditionally secure relativistic multi-party biased coin flipping and die rolling

We introduce relativistic multi-party biased die-rolling protocols, generalizing coin flipping to M≥2 parties and to N≥2 outcomes for any chosen outcome biases and show them unconditionally secure. Our results prove that the most general random secure multi-party computation, where all parties receive the output and there is no secret input by any party, can be implemented with unconditional security. Our protocols extend Kent's (Kent A. 1999 Phys. Rev. Lett.83, 5382) two-party unbiased coin-flipping protocol, do not require any quantum communication, are practical to implement with current technology and to our knowledge are the first multi-party relativistic cryptographic protocols.
Misalignment Tolerant Diffractive Optical Networks

Design of diffractive optical networks that are resilient against physical misalignments is reported. The success of this design framework is also experimentally demonstrated using 3D printed diffractive networks that operate at THz wavelengths.
The effectiveness of using digital platforms to practice English during the COVID-19 crisis as perceived by Ecuadorian students

Purpose This mixed-methods research aimed to investigate students’ opinions on the effectiveness of using digital platforms to carry on their learning process. Design/methodology/approach This is a mixed-methods research. One hundred students, registered in the last English course of their curriculum, took part in the investigation. They filled out a Likert-scale survey using the criteria for CALL evaluation. During the data analysis of the quantitative section of the research, a chi-square of 15.0672 and a p -value of 0.519719 were obtained, making this result not significant at p &lt; 0.05. A Levene test of variance equality was performed on the resulting data to confirm the results. Personal interviews were carried out to triangulate the previous results. Findings This study determined that Ecuadorian undergraduate students have a negative perception of the usefulness of using a digital class to learn English. These results have important implications for teachers who must work harder during these times of COVID-19 to attain students' attention. Research limitations/implications This research is limited by its conception of qualitative methods. This limitation also opens the door for further studies. The quantitative and mixed methods studies are suggested to confirm the results obtained here. Practical implications This study has practical implications for teachers and language center managers. They can use the information attained to adapt their teachings in order to improve these results. Managers will benefit from it as they can plan for teacher training considering the comments given by students. Social implications The social implication of this study is that the students, through their comments, have implied the need of having some sort of socialization and ERT does not permit such. Originality/value This paper has value as it closes the gap of information regarding the use of this new teaching modality attained from Latin American countries and more specifically from Ecuador.
Visualization of User’s Behavior in Indoor Virtual Environments Through Interactive Heatmaps

Three-dimensional virtual environments (VEs), such as those used in video games and virtual reality experiences, pose new challenges to the study of user’s behavior. This paper proposes a system based on the combination of two heatmaps for the analysis of user’s movement in the VE and of the areas looked at by him/her. It also describes a pilot study aimed at assessing the efficacy of the system. Results of the study indicate that the system can effectively support analysts in identifying user’s look-at behaviors as well as navigation strategies, patterns, and coverage of specific areas during movement.
Formation of a Developmental Environment of Professional Training of Future Music Teachers in the Ukrainian Educational Space

The article proves that the problem of creating a developmental environment in the course of professional training of future music teachers is now becoming a particularly acute issue and is becoming an important area of scientific and pedagogical research. The article aims to theoretically substantiate, develop in the form of a model, implement and experimentally verify pedagogical conditions for the formation of a developmental environment for the professional training of future music teachers at music and pedagogical faculties. The aim of the article was achieved by using the following research methods: pedagogical observation, questionnaires, conversations with students and teachers, psychological and pedagogical methods for analysing the style of pedagogical activity and communication between teachers and students. The effectiveness of the formation of a developmental environment for the professional training of future music teachers increased under the following conditions: ensuring the facilitative nature of interpersonal interaction in the system "teacher –music –student", organizing the professional training process for future music teachers based on developmental teaching; actualization of the developmental potential of special music disciplines; activation of students' independent work; formation of a creative, psychologically comfortable atmosphere at the faculty. The formative experiment and the results of the pedagogical diagnostics have proved the validity of the developed model for changing educational conditions and the prevailing high level of creative independence and professional readiness of future music teachers in the experimental group.
Digital Literacy: Teachers' Perceptions of Using Google Accounts in the Online Learning Process

This current study explores the teacher's perspective on the usefulness of google account in the online learning process at SMAN 1 Tambang, Riau. The teachers and other school staff did not simply implement the policy of the distance learning process. The shift from face-to-face learning activities to online learning is confusing for teachers, especially senior teachers. Teachers are challenged to be able to use technology in carrying out their duties. The digital literacy of teachers is critical in the implementation of online learning. Hence, an understanding of google classroom, google meet, and google form is an influential media aid in the learning process, especially in online learning. The research method used is a survey method. The sample in this study was 46 teachers of SMAN 1 Tambang. The results showed that 46 respondents who filled out the research questionnaire, 75% stated Strongly Agree, 23% stated Agree, and 2% stated Disagree that google accounts were instrumental in the process of online learning during the Covid-19 pandemic. This result shows that the Google account is an effective learning media used in the online learning process during the Covid-19 pandemic. Therefore, teacher mastery in the use of Google accounts is part of digital literacy that needs improvement.
Application of a methodological approach to compare ontologies

Purpose This study aims to the OQuaRE quality model to the developed methodology. Design/methodology/approach Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them. Findings In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain. Practical implications To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains. Originality/value Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web.
Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)

The detection of, explanation of, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. They are applied in various guises including anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, and open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. The techniques developed have been applied in a wide range of domains including fraud detection and anti-money laundering in fintech, early disease detection, intrusion detection in large-scale computer networks and data centers, defending AI systems from adversarial attacks, and in improving the practicality of agents through overcoming the closed-world assumption.
Sensitivity Analysis for 3E Assessment of BIPV System Performance in Abadan in Southwestern Iran

In Iran, due to the problems and constraints of fossil fuels and the need to maximize the use of solar potential, one of the best ways is the application of photovoltaic systems integrated with buildings. Due to the significant dependence of solar cell performance on the availability of radiation, it is necessary for architects to have an accurate assessment of the amount of electricity produced in different conditions. Therefore, in the present work, using HOMER software, the energy-econo-Enviro (3E) potential of a Building Integrated Photovoltaic (BIPV) in Abadan was studied. The effect of slope and azimuth of solar cells as well as cloudiness and system losses were investigated using sensitivity analysis. The results showed that the PV-grid system was the most economical option and after the azimuth angle of zero degree, the positive azimuth angle was the most economical. The results also showed that the slope of 30 degree and the angle of azimuth equal to zero was appropriate, for which the price per kWh of generated electricity was calculated to be $0.09. For the use of solar cells in the vertical wall of the building, the southwest direction was the most suitable and based on the results, it was suggested that the western wall of the building should be in the form of “inclined PVs with windows”. The authors of this paper hope that the results of the present work can be used by architects and energy decision-makers as a guide in developing the BIPV use in Iran.
Indian Sign Language Recognition Using Python

In this paper, an Indian sign language recognition using Python program has been developed. This work was taken up by keeping in mind the difficulties that are faced by differently abled people, for instance, people who cannot speak, or those who cannot hear. The code has been written in Python and trained using various modules like Tensorflow, Keras, and operating system module (Os), OpenCv (Cv2), Numpy, and various preprocessors. The training has been done using a personally created database of symbols in Indian sign language (ISL) such as digits 0–9 as well as an online database from GitHub to improve accuracy. The results obtained were compiled in Anaconda 3.0 and then finally tested. This framework can help differently abled people communicate better with other individuals around them.
Distributed Enumeration of Four Node Graphlets at Quadrillion-Scale

Graphlet enumeration is a basic task in graph analysis with many applications. Thus it is important to be able to perform this task within a reasonable amount of time. However, this objective is challenging when the input graph is very large, with millions of nodes and edges. Known solutions are limited in terms of scalability. Distributed computing is often proposed as a solution to improve scalability. However, it has to be done carefully to reduce the overhead cost and to really benefit from the distributed solution. We study the enumeration of four-node graphlets in undirected graphs using a distributed platform. We propose an efficient distributed solution which significantly surpasses the existing solutions. With this method we are able to process larger graphs that have never been processed before and enumerate quadrillions of graphlets using a modest cluster of machines. We show the scalability of our solution through experimental results. Finally, we also extend our algorithm to enumerate graphlets in probabilistic graphs and demonstrate its suitability for this case.
Bioethics of public commenting: Manipulation, data risk, and public participation in E‐Rulemaking

Little scholarly attention has been given to the ethics of public commenting as part of the online federal rule-making process. This essay argues the process of public commenting on federal regulations in the digital era threatens both the integrity of those regulations and the integrity of the individuals they are meant to protect. The ongoing risk is anonymous public commenting is open to manipulation. This risk is particularly salient for eRulemaking with implications for human subjects as was shown in the completed revision process to the Common Rule. Guarding against physical and informational harms requires at least some verification of the identities of public commenters. The burdens of verification access are outweighed by the benefits to authentic participation in e-Rulemaking.
Learning Explicit Prosody Models and Deep Speaker Embeddings for Atypical Voice Conversion

Though significant progress has been made for the voice conversion (VC) of typical speech, VC for atypical speech, e.g., dysarthric and second-language (L2) speech, remains a challenge, since it involves correcting for atypical prosody while maintaining speaker identity. To address this issue, we propose a VC system with explicit prosodic modelling and deep speaker embedding (DSE) learning. First, a speech-encoder strives to extract robust phoneme embeddings from atypical speech. Second, a prosody corrector takes in phoneme embeddings to infer typical phoneme duration and pitch values. Third, a conversion model takes phoneme embeddings and typical prosody features as inputs to generate the converted speech, conditioned on the target DSE that is learned via speaker encoder or speaker adaptation. Extensive experiments demonstrate that speaker adaptation can achieve higher speaker similarity, and the speaker encoder based conversion model can greatly reduce dysarthric and non-native pronunciation patterns with improved speech intelligibility. A comparison of speech recognition results between the original dysarthric speech and converted speech show that absolute reduction of 47.6% character error rate (CER) and 29.3% word error rate (WER) can be achieved.
Generalised GMW Sequences

Families of binary sequences with low correlation are required in applications such as wireless communications, ranging and time delay measurement and digital watermarking, among others. Many constructions have been proposed that employ m-sequences as basic building blocks, such as the Gordon-Mills-Welch (GMW) sequences. In this work we present a unified construction of GMW sequences derived from suitable m-sequences by using the method of composition, producing sequences of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{n}-1$</tex> with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> being an integer composite number. A given m-sequence is folded using the Chinese remainder theorem (CRT) into a two dimensional array, whose columns are either constant or cyclic shifts of a short <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> -sequence. Then, the array can be summarized by a short <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> -sequence and the sequence of shifts (shift sequence). By looking at array interpretation, we avoid using Trace representation of intermediate fields, which makes its implementation more straightforward and secure. Pseudonoise arrays can be produced by constructing all the valid columns and shift sequences, the latter obtained by proper decimations and proper multiplication. Equivalences are removed by selecting a cyclotomic set leader from the degenerate conjugacy class. The window properties of these sequences can be exploited to construct generalized GMW sequence generators for lengths as large as the long codes in GPS i.e. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{42}-1$</tex> . A similar generalisation of the small Kasami sets can be constructed using this algorithm. The complexity of the algorithms is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sqrt{L}$</tex> where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> is the length of the sequence, improving known algorithms. Finally, the shift sequences constructed in this work are good candidates to frequency hopping and time hopping sequences in UWB wireless communications and localisation systems.
A Projection Neural Network to Nonsmooth Constrained Pseudoconvex Optimization

In this article, a single-layer projection neural network based on penalty function and differential inclusion is proposed to solve nonsmooth pseudoconvex optimization problems with linear equality and convex inequality constraints, and the bound constraints, such as box and sphere types, in inequality constraints are processed by projection operator. By introducing the Tikhonov-like regularization method, the proposed neural network no longer needs to calculate the exact penalty parameters. Under mild assumptions, by nonsmooth analysis, it is proved that the state solution of the proposed neural network is always bounded and globally exists, and enters the constrained feasible region in a finite time, and never escapes from this region again. Finally, the state solution converges to an optimal solution for the considered optimization problem. Compared with some other existing neural networks based on subgradients, this algorithm eliminates the dependence on the selection of the initial point, which is a neural network model with a simple structure and low calculation load. Three numerical experiments and two application examples are used to illustrate the global convergence and effectiveness of the proposed neural network.
Plausibility Analysis of Shift-Sum Decoding for Cyclic Codes

Using the minimum weight dual codewords (MWDCs) of a cyclic code, the shift-sum decoding can correct errors beyond half of the code's minimum Hamming distance. It utilizes the frequency of the syndrome polynomials' coefficients to identify the erroneous positions and correct the errors. This paper analyzes the plausibility of the shift-sum decoding for both binary and non-binary cyclic codes. It first determines the probability distributions of the frequency of the syndrome polynomials' coefficients as well as their expected values for the erroneous and non-erroneous positions. Based on these characterizations, this work further provides an analysis for the iterative shift-sum decoding, unveiling the statistical rationale on the shift-sum decoding's capability of correcting errors beyond the half distance bound.
End-to-End Open Vocabulary Keyword Search

Recently, neural approaches to spoken content retrieval have become popular. However, they tend to be restricted in their vocabulary or in their ability to deal with imbalanced test settings. These restrictions limit their applicability in keyword search, where the set of queries is not known beforehand, and where the system should return not just whether an utterance contains a query but the exact location of any such occurrences. In this work, we propose a model directly optimized for keyword search. The model takes a query and an utterance as input and returns a sequence of probabilities for each frame of the utterance of the query having occurred in that frame. Experiments show that the proposed model not only outperforms similar end-to-end models on a task where the ratio of positive and negative trials is artificially balanced, but it is also able to deal with the far more challenging task of keyword search with its inherent imbalance. Furthermore, using our system to rescore the outputs an LVCSR-based keyword search system leads to significant improvements on the latter.
Research on Aircraft Ditching State Selection Based on Floating Characteristics of Water Surface

Abstract When an airplane is forced to ditching on water, slight state changes, such as weight, center of gravity, initial attitude, etc., will have great influence on the ditching performance and floating ability of the airplane. In order to study the floating ability of the aircraft under the above parameters, this paper takes a fixed-wing aircraft as the basis model, and based on hydrostatics, divides the water inflow into several tiny water inflow segments. By studying the floating attitude and floating time of different aircraft states, the influence law of the center of gravity position parameters on the floating time is mastered. The research shows that the floating time and water inflow volume of the aircraft will be significantly increased when the higher center of gravity position and the non-most unfavorable longitudinal range of the center of gravity are adopted, and forced landing on water in this state can provide emergency evacuation and escape for the aircraft personnel.