Reviewerly/exHarmony huggingface dataset:
A Color Image Encryption Algorithm with Cat Map and Chaos Map Embedded

In order to deal with the problem of encryption algorithms being overly simplistic, and the relatively low security of color images that creates potential to be attacked in the transmission process, this paper will introduce a new encryption algorithm that is designed to divide color images into R, G and B layers. In the scrambling operation: the first scrambling is aimed to block the clear text image scrambling; The second scrambling is the dynamic Arnold scrambling of the ciphertext after the first scrambling. In the diffusion operation, the scrambled ciphertext image was taken as the input, and the pseudo-random sequence generated by Tent mapping and Sine mapping was embedded. The sequence generated by Logistic mapping was used to select sub-blocks for block diffusion of the image. Tent-Sine mapping was applied to the second diffusion to obtain the final ciphertext image. The algorithm designed in this paper combines image block scrambling and dynamic Arnold scrambling, the scrambling degree of each layer of image pixels would be greatly improved, thus improving the security of color images. In the process of diffusion, chaos sequence is selected for diffusion operation, which increases the difficulty of decoding ciphertext. The simulation results show that the new algorithm has desirable encryption effect, strong key sensitivity and large key space, and complex encryption algorithm can effectively resist attacks, which certainly has value in image information security.
Immersive telepresence on the operation of unmanned vehicles

Unmanned ground vehicles are usually deployed in situations, where it is too dangerous or not feasible to have an operator onboard. One challenge faced when such vehicles are teleoperated is maintaining a high situational awareness, due to aspects such as limitation of cameras, characteristics of network transmission, and the lack of other sensory information, such as sounds and vibrations. Situation awareness refers to the understanding of the information, events, and actions that will impact the execution and the objectives of the tasks at the current and near future of the operation of the vehicle. This work investigates how the simultaneous use of immersive telepresence and mixed reality could impact the situation awareness of the operator and the navigation performance. A user study was performed to compare our proposed approach with a traditional unmanned vehicle control station. Quantitative data obtained from the vehicle’s behavior and the situation awareness global assessment technique were used to analyze such impacts. Results provide evidence that our approach is relevant when the task requires a detailed observation of the surroundings, leading to higher situation awareness and navigation performance.
PMI-Masking: Principled masking of correlated spans

Masking tokens uniformly at random constitutes a common flaw in the pretraining of Masked Language Models (MLMs) such as BERT. We show that such uniform masking allows an MLM to minimize its training objective by latching onto shallow local signals, leading to pretraining inefficiency and suboptimal downstream performance. To address this flaw, we propose PMI-Masking, a principled masking strategy based on the concept of Pointwise Mutual Information (PMI), which jointly masks a token n-gram if it exhibits high collocation over the corpus. PMI-Masking motivates, unifies, and improves upon prior more heuristic approaches that attempt to address the drawback of random uniform token masking, such as whole-word masking, entity/phrase masking, and random-span masking. Specifically, we show experimentally that PMI-Masking reaches the performance of prior masking approaches in half the training time, and consistently improves performance at the end of pretraining.
Dephasing Process of a Single Atom Interacting with a Two-Mode Field

We consider the interaction of a qubit system with a two-mode field in the presence of multi-photon transition and phase damping effect. We use the master equation to obtain the density operator when the qubit is initially prepared in its excited state and the field is in a finite-dimensional pair coherent state. The properties of the considered system, such as the population inversion, amount of the mixedness, parameter estimation, and squeezing, are explored for one- and two-photon transitions. The effects of photon addition to the field and phase damping on the evaluation of these quantumness measures are also investigated.
Trend and Variability Analysis of Sunshine Duration in Divisional Headquarters of Bangladesh

In this study, Sunshine duration data of eight divisional headquarters of Bangladesh Meteorological Stations (Dhaka, Rangpur, Rajshahi, Mymensingh, Sylhet, Barishal, Khulna and Chattagram) were analyzed to evaluate the long-term changes and trends. The data used are the BMD data spanning from 1980 to 2010. The annual sunshine duration has decreased by the month of (June –September) from 1980 to 2010. Seasonal changes in sunshine duration were also analyzed where the maximum decline was found in rainy-monsoon season (June – September), the winter season (December – February), then the post-monsoon season (October – November) and the minimum in the pre-monsoon season (March – May). Analysis of observed data before and after 2000 represents the sunshine durations have decreasing trends in all divisional headquarters of Bangladesh except Chattagram station during the month of (January – December). General Circulation Model (GCM) defined that the maximum sunshine hour was decline north-east and south-west in all divisional headquarters of Bangladesh during the month of (June – September) and the minimum sunshine hour was decline in eastern part of the country during the month of (March – May).&#x0D; Environ. Sci. &amp; Natural Resources, 12(1&amp;2): 127-133, 2019
Many-to-Many Unsupervised Speech Conversion From Nonparallel Corpora

We address a nonparallel data-driven many-to-many speech modeling and multimodal style conversion method. In this work, we train a speech conversion model for multiple domains rather than a specific source and target domain pair, and we generate diverse output speech signals from a given source domain speech by transferring some speech style-related characteristics while preserving its linguistic content information. The proposed method comprises a variational autoencoder (VAE)-based many-to-many speech conversion network with a Wasserstein generative adversarial network (WGAN) and a skip-connected autoencoder-based self-supervised learning network. The proposed conversion network trains the models by decomposing the spectral features of the input speech signal into a content factor that represents domain-invariant information and a style factor that represents domain-related information to automatically estimate the various speech styles of each domain, and the network converts the input speech signal to another domain using the computed content factor with the target style factor we want to change. Diverse and multimodal outputs can be generated by sampling different style factors. We also train models in a stable manner and improve the quality of generated outputs by sharing the discriminator of the VAE-based speech conversion network and that of the self-supervised learning network. We apply the proposed method to speaker conversion and perform the perceptual evaluations. Experimental results revealed that the proposed method obtained high accuracy of converted spectra, significantly improved the sound quality and speaker similarity of the converted speech, and contributed to stable model training.
Dicoelitid belemnites from the Caucasian margin of the Tethys: new data from the Upper Bajocian-Lower Bathonian of Karachay-Cherkessia, southwest Russia

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Multiple Time Series Perceptive Network for User Tag Suggestion in Online Innovation Community

User tag suggestion technique, aiming at learning users' preferences over knowledge products from their historical behaviors, plays an important role in generating personalized recommendation in online innovation community. However, most current user tagging solutions only utilize a single kind of behavior to predict a single tag for users, resulting in weak generalization of user profile. In this paper, we propose a multiple time series perceptive network (MTSPN) for user tagging tasks in online innovation community. In particular, MTSPN takes multiple kinds of user behaviors into consideration for collaborative perception purpose, in which multi-scale sequential features are extracted from different sequential behaviors, and a multi-label classification module is built-in the proposed MTSPN model to predict multiple tags for users. Our encouraging experimental results on a real-world dataset collected from “Thingiverse” community validate the superiority of the our MTSPN model over several existing user tagging methods.
A Security Cost Modelling Framework for Cyber-Physical Systems

Cyber-Physical Systems (CPS) are formed through interconnected components capable of computation, communication, sensing and changing the physical world. The development of these systems poses a significant challenge, since they have to be designed in a way to ensure cyber-security without impacting their performance. This article presents the Security Cost Modelling Framework (SCMF) and shows supported by an experimental study how it can be used to measure, normalise, and aggregate the overall performance of a CPS. Unlike previous studies, our approach uses different metrics to measure the overall performance of a CPS and provides a methodology for normalising the measurement results of different units to a common Cost Unit . Moreover, we show how the Security Costs can be extracted from the overall performance measurements, which allows us to quantify the overhead imposed by performing security-related tasks. Furthermore, we describe the architecture of our experimental testbed and demonstrate the applicability of SCMF in an experimental study. Our results show that measuring the overall performance and extracting the security costs using SCMF can serve as basis to redesign interactions to achieve the same overall goal at less costs.
English Teaching Ability Evaluation Algorithm Based on Big Data Fuzzy K-means Clustering

Since entering the new era, with the rapid development of science and technology, the daily data generated by all walks of life is an astronomical number. The amount of data generated now has explosive growth compared to before. Under such a development environment, if there is a simple, convenient, and effective way to find the corresponding information you want to find from the massive amount of miscellaneous data, then through this way you can be ahead of others and provide a very important solid foundation for your own development. Based on the traditional English teaching ability assessment method, there are a series of data processing problems, and the accuracy rate is insufficient. This paper proposes a new fuzzy K-means clustering algorithm combined with big data. This algorithm is combined with information. New English teaching ability assessment algorithm. Realize a new English teaching ability assessment algorithm. This article mainly analyzes the clustering algorithm under the current big data background, discusses the current situation of English teaching ability assessment, and puts forward a series of suggestions on how to optimize and enhance the English teaching ability assessment algorithm. And try to find a new way out of the English teaching ability assessment algorithm in the current era based on the results of these studies. According to the experimental results, the new algorithm proposed in this paper for English teaching ability assessment has better information fusion analysis ability than the traditional English teaching ability algorithm, which greatly improves the accuracy of teaching ability assessment and the application of teaching resources the efficiency has also been greatly improved.
A Simple Algorithm for Optimal Search Trees with Two-way Comparisons

We present a simple O(n 4 ) -time algorithm for computing optimal search trees with two-way comparisons. The only previous solution to this problem, by Anderson et al., has the same running time but is significantly more complicated and is restricted to the variant where only successful queries are allowed. Our algorithm extends directly to solve the standard full variant of the problem, which also allows unsuccessful queries and for which no polynomial-time algorithm was previously known. The correctness proof of our algorithm relies on a new structural theorem for two-way-comparison search trees.
Building a training dataset for classification under a cost limitation

Purpose A necessary preprocessing of document classification is to label some documents so that a classifier can be built based on which the remaining documents can be classified. Because each document differs in length and complexity, the cost of labeling each document is different. The purpose of this paper is to consider how to select a subset of documents for labeling with a limited budget so that the total cost of the spending does not exceed the budget limit, while at the same time building a classifier with the best classification results. Design/methodology/approach In this paper, a framework is proposed to select the instances for labeling that integrate two clustering algorithms and two centroid selection methods. From the selected and labeled instances, five different classifiers were constructed with good classification accuracy to prove the superiority of the selected instances. Findings Experimental results show that this method can establish a training data set containing the most suitable data under the premise of considering the cost constraints. The data set considers both “data representativeness” and “data selection cost,” so that the training data labeled by experts can effectively establish a classifier with high accuracy. Originality/value No previous research has considered how to establish a training set with a cost limit when each document has a distinct labeling cost. This paper is the first attempt to resolve this issue.
Artificial intelligence and text and data mining

Text and Data Mining (TDM) is a key tool for enabling artificial intelligence (AI) innovation. As the BlueDot project has demonstrated, the possibility to mine works and data to identify patterns and correlations might be crucial for solving pressing societal problems (e.g., the prediction of the Covid-19 outbreak). The importance of TDM has been understood by the European legislator, which has introduced two specifically tailored exceptions in the Copyright in the Digital Single Market Directive. This chapter argues that those new provisions still present several flaws that risk to stifle AI developments in Europe. The present chapter introduces an interpretative framework, based on the analysis of the infringement test, to rethink the rights of reproduction and extraction in line with the economic rationale of copyright and the database right. Furthermore, it makes suggestions to improve the TDM exceptions at national level. In conclusion, this chapter outlines the remaining challenges of private ordering and trade secrets for research and AI innovation.
Group theory on quantum Boltzmann machine

Group theory is extremely successful in characterizing the symmetries in quantum systems, which greatly simplifies and unifies our treatments of quantum systems. Here we introduce the concept of the symmetry for a quantum Boltzmann machine and develop a group theory to describe the symmetry. This symmetry implies not only that all the target states related with the symmetry transformations are equivalent, but also that for a given target state all the optimal solutions related with the symmetry transformations that keeps the target state invariant are equivalent. For the Boltzmann machines built on qubits, we propose a systematic procedure to construct the group, and develop a numerical algorithm to verify the completeness of our construction.
A Comparison of Three Ways to Measure Time-Dependent Densities With Quantum Simulators

Quantum algorithms are touted as a way around some classically intractable problems such as the simulation of quantum mechanics. At the end of all quantum algorithms is a quantum measurement whereby classical data is extracted and utilized. In fact, many of the modern hybrid-classical approaches are essentially quantum measurements of states with short quantum circuit descriptions. Here, we compare and examine three methods of extracting the time-dependent one-particle probability density from a quantum simulation: direct Z -measurement, Bayesian phase estimation, and harmonic inversion. We have tested these methods in the context of the potential inversion problem of time-dependent density functional theory. Our test results suggest that direct measurement is the preferable method. We also highlight areas where the other two methods may be useful and report on tests using Rigetti's quantum virtual device. This study provides a starting point for imminent applications of quantum computing.
Research on Modeling of Aircraft-Level High-lift System Architecture Based on SysML

Abstract The aircraft-level high-lift system architecture of civil aircraft includes functional architecture, logical architecture and physical architecture. The system architecture describes the hierarchical relationship, cross-linking relationship and information interaction between the function, logic and physics of the system. This article is based on the SysML modeling language, using the MagicDraw modeling tool to transform the traditional design work based on experience and documents into model-based architecture design work, using the association and traceability of the model to ensure the integrity of the information in the design process. This article establishes the design relationship between its functional, logical and physical layers of the high-lift system to verify the efficiency of this modeling method.
Discounted Sampling Policy Gradient for Robot Multi-objective Visual Control

Robot visual control often involves multiple objectives such as achieving high efficiency, maintaining stability, and avoiding failure. This paper proposes a novel Vision-Based Control method (VBC) with the Discounted Sampling Policy Gradient (DSPG) and Cosine Annealing (CA) to achieve excellent multi-objective control performance. In our proposed visual control framework, a DSPG learning agent is employed to learn a policy estimating continuous kinematics for VBC. The deep policy maps the visual observation to a specific action in an end-to-end manner. The DSPG agent finally can update the policy to obtain the optimal or near-optimal solution using shaped rewards from the environment. The proposed VBC-DSPG model is optimized using a heuristic method. Experimental results demonstrate that the proposed method performs very well compared with some classical competitors in the multi-objective visual control scenario.
Defining Utility Functions for Multi-stakeholder Self-adaptive Systems

[Context and motivation:] For realistic self-adaptive systems, multiple quality attributes need to be considered and traded off against each other. These quality attributes are commonly encoded in a utility function, for instance, a weighted sum of relevant objectives. [Question/problem:] The research agenda for requirements engineering for self-adaptive systems has raised the need for decision-making techniques that consider the trade-offs and priorities of multiple objectives. Human stakeholders need to be engaged in the decision-making process so that the relative importance of each objective can be correctly elicited. [Principal ideas/results:] This research preview paper presents a method that supports multiple stakeholders in prioritizing relevant quality attributes, negotiating priorities to reach an agreement, and giving input to define utility functions for self-adaptive systems. [Contribution:] The proposed method constitutes a lightweight solution for utility function definition. It can be applied by practitioners and researchers who aim to develop self-adaptive systems that meet stakeholders’ requirements. We present details of our plan to study the application of our method using a case study.
Reparation in Evolutionary Algorithms for Multi-objective Feature Selection in Large Software Product Lines

Software Product Lines Engineering is the area of software engineering that aims to systematise the modelling, creation and improvement of groups of interconnected software systems by formally expressing possible alternative products in the form of Feature Models. Deriving a software product/system from a feature model is called Feature Configuration. Engineers select the subset of features (software components) from a feature model that suits their needs, while respecting the underlying relationships/constraints of the system–which is challenging on its own. Since there exist several (and often antagonistic) perspectives on which the quality of software could be assessed, the problem is even more challenging as it becomes a multi-objective optimisation problem. Current multi-objective feature selection in software product line approaches (e.g., SATIBEA) combine the scalability of a genetic algorithm (IBEA) with a solution reparation approach based on a SAT solver or one of its derivatives. In this paper, we propose MILPIBEA, a novel hybrid algorithm which combines IBEA with the accuracy of a mixed-integer linear programming (MILP) reparation. We show that the MILP reparation modifies fewer features from the original infeasible solutions than the SAT reparation and in a shorter time. We also demonstrate that MILPIBEA outperforms SATIBEA on average on various multi-objective performance metrics, especially on the largest feature models. The other major challenge in software engineering in general and in software product lines, in particular, is evolution. While the change in software components is common in the software engineering industry, the particular case of multi-objective optimisation of evolving software product lines is not well-tackled yet. We show that MILPIBEA is not only able to better take advantage of the evolution than SATIBEA, but it is also the one that continues to improve the quality of the solutions when SATIBEA stagnates. Overall, IBEA performs better when combined with MILP instead of SAT reparation when optimising the multi-objective feature selection in large and evolving software product lines.
The Use of Static Telecytological Applications for Proficiency Testing Purposes in the Field of Diagnostic Cytopathology

Over the last decade, the practice of clinical cytopathology was dramatically influenced by the wide implementation of informatics and computer sciences into the laboratory workflow. New types of cameras and microscopes connected to computers made possible image capture and transmission (telecytology). Remote cytological diagnosis can nowadays be achieved either with the use of cytological pictures viewed in real-time from the microscope (dynamic telecytological systems) or with the use of cytological pictures that are first captured in a digital format and then transmitted using a store-and-forward approach to distant observers (static telecytological systems). ISO 15189:2012 for medical laboratories requires successful participation in proficiency testing programs. This article emphasizes on the feasibility of using static telecytological applications in order to implement a proficiency test for cytopathology labs wishing to be accredited according to ISO 15189:2012.