Reviewerly/exHarmony huggingface dataset:
Multi-photon resonances in Josephson junction-cavity circuits

Abstract We explore the dissipative dynamics of nonlinearly driven oscillator systems tuned to resonances where multiple excitations are generated. Such systems are readily realised in circuit QED systems combining Josephson junctions with a microwave cavity and a drive achieved either through flux or voltage bias. For resonances involving three or more photons the system undergoes a sequence of two closely spaced dynamical transitions (the first one discontinuous and the second continuous) as the driving is increased leading to steady states that form complex periodic structures in phase space. In the vicinity of the transitions the system displays interesting bistable behaviour: we find that coherent effects can lead to surprising oscillations in the weight of the different dynamical states in the steady state of the system with increasing drive. We show that the dynamics is well-described by a simple effective rate model with transitions between states localised at different points in the phase space crystal. The oscillations in the weights of the dynamical states is reflected in corresponding oscillations in a time-scale that describes transitions between the states.
Adversarial Edge-Aware Image Colorization With Semantic Segmentation

It has become a trend in recent years to use deep neural networks for colorization.However, previous methods often encounter problems with edge color leakage and difficulties in obtaining a plausible color output from the Euclidean distance.To solve these problems, we propose a new adversarial edgeaware image colorization method with multitask output combined with semantic segmentation.The system uses a generator with a deep semantic fusion structure to infer semantic clues in a given grayscale image under chroma conditions and learns colorization by simultaneously predicting color information and semantic information.In addition, we also use a specific color difference loss with characteristics of human visual observation that is combined with semantic segmentation loss and adversarial loss for training.The experimental results show that our method is superior to existing methods in terms of different quality metrics and achieves good results in image colorization.
Driving Through Ghosts: Behavioral Cloning with False Positives

Safe autonomous driving requires robust detection of other traffic participants. However, robust does not mean perfect, and safe systems typically minimize missed detections at the expense of a higher false positive rate. This results in conservative and yet potentially dangerous behavior such as avoiding imaginary obstacles. In the context of behavioral cloning, perceptual errors at training time can lead to learning difficulties or wrong policies, as expert demonstrations might be inconsistent with the perceived world state. In this work, we propose a behavioral cloning approach that can safely leverage imperfect perception without being conservative. Our core contribution is a novel representation of perceptual uncertainty for learning to plan. We propose a new probabilistic birds-eye-view semantic grid to encode the noisy output of object perception systems. We then leverage expert demonstrations to learn an imitative driving policy using this probabilistic representation. Using the CARLA simulator, we show that our approach can safely overcome critical false positives that would otherwise lead to catastrophic failures or conservative behavior.
Designing Student Attendance Information Systems Web-Based

Curug SMPN 1 is one of the state senior high schools in Tangerang regency, precisely in Curug district. The teacher absentee system in implementing learning has been running and is considered ineffective because it still has to record in books which are considered inefficient. To facilitate the need for teachers to record reports in books and to make it easier for supervisors, a web-based information system design was created. The method used is a prototype model using the help of an application from Adobe. With the prototype it is expected that the use of the WhatsApp service can be used to provide information to parents. The use of this prototype method is expected to be developed in other schools.
Fuzzy Hoeffding Decision Tree for Data Stream Classification

Data stream mining has recently grown in popularity, thanks to an increasing number of applications which need continuous and fast analysis of streaming data.Such data are generally produced in application domains that require immediate reactions with strict temporal constraints.These particular characteristics make problematic the use of classical machine learning algorithms for mining knowledge from these fast data streams and call for appropriate techniques.In this paper, based on the well-known Hoeffding Decision Tree (HDT) for streaming data classification, we introduce FHDT, a fuzzy HDT that extends HDT with fuzziness, thus making HDT more robust to noisy and vague data.We tested FHDT on three synthetic datasets, usually adopted for analyzing concept drifts in data stream classification, and two real-world datasets, already exploited in some recent researches on fuzzy systems for streaming data.We show that FHDT outperforms HDT, especially in presence of concept drift.Furthermore, FHDT is characterized by a high level of interpretability, thanks to the linguistic rules that can be extracted from it.
Quantum machine learning with differential privacy

Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer. Many datasets used in machine learning are crowd sourced or contain some private information, but to the best of our knowledge, no current QML models are equipped with privacy-preserving features. This raises concerns as it is paramount that models do not expose sensitive information. Thus, privacy-preserving algorithms need to be implemented with QML. One solution is to make the machine learning algorithm differentially private, meaning the effect of a single data point on the training dataset is minimized. Differentially private machine learning models have been investigated, but differential privacy has not been thoroughly studied in the context of QML. In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm. This marks the first proof-of-principle demonstration of privacy-preserving QML. The experiments demonstrate that differentially private QML can protect user-sensitive information without signficiantly diminishing model accuracy. Although the quantum model is simulated and tested on a classical computer, it demonstrates potential to be efficiently implemented on near-term quantum devices [noisy intermediate-scale quantum (NISQ)]. The approach's success is illustrated via the classification of spatially classed two-dimensional datasets and a binary MNIST classification. This implementation of privacy-preserving QML will ensure confidentiality and accurate learning on NISQ technology.
Are Stablecoins Safe Havens for Traditional Cryptocurrencies? An Empirical Study during the COVID-19 Pandemic

We investigate whether stablecoins are safe havens for traditional cryptocurrencies with fresh evidence from the recent crisis period of the COVID-19 pandemic. Our results support the safe-haven properties of Tether for both before and during the pandemic. For Digix, a gold-backed stablecoin with relatively small market capitalization, we find a change in characteristics before and during the pandemic, but do not find statistically significant evidence for its safe-haven properties. Furthermore, we document that, when considering the economic benefits and costs of adding safe-haven assets into cryptocurrency portfolios, the one with Tether outperforms both a naked portfolio and the portfolio with a traditional safe-haven asset such as gold.
A Novel Neural Network Training Algorithm for the Identification of Nonlinear Static Systems: Artificial Bee Colony Algorithm Based on Effective Scout Bee Stage

In this study, a neural network-based approach is proposed for the identification of nonlinear static systems. A variant called ABCES (ABC Based on Effective Scout Bee Stage) is introduced for neural network training. Two important changes are carried out with ABCES. The first is an update of “limit” control parameters. In ABC algorithm, “limit” value is fixed. It is adaptively adjusted according to number of iterations in ABCES. In this way, the efficiency of the scout bee stage is increased. Secondly, a new solution-generating mechanism for the scout bee stage is proposed. In ABC algorithm, new solutions are created randomly. It is aimed at developing previous solutions in the scout bee stage of ABCES. The performance of ABCES is analyzed on two different problem groups. First, its performance is evaluated on 13 numerical benchmark test problems. The results are compared with ABC, GA, PSO and DE. Next, the neural network is trained by ABCES to identify nonlinear static systems. 6 nonlinear static test problems are used. The performance of ABCES in neural network training is compared with ABC, PSO and HS. The results show that ABCES is generally effective in the identification of nonlinear static systems based on neural networks.
Review of Decentralized Finance Applications and Their Total Value Locked

Currently, in the world there is a growing interest in the digital economy including the blockchain technology. Decentralized Finance (DeFi) is one of the leading current blockchain technology-related trends. The paper aims to provide an overview of the opportunities, advantages and shortcomings of this technology, as well as to summarize information on 12 most popular DeFi applications, using Total Value Locked indicators for the period of 34 months.
Web Map Effectiveness in the Responsive Context of the Graphical User Interface

The main objective of this article was to determine the effectiveness of a web map GUI (Graphical User Interface) layout designed specifically for desktop monitors and smartphones. A suitable design of buttons for the graphical user interface is vital for the effectiveness of web maps. This article presents a study of three rules that prevail in GUI map design in terms of responsiveness, which was analyzed on two devices: a smartphone and a PC screen. The GUI effectiveness study, based on six variants of web maps, was conducted by means of eye-tracking on a group of 120 participants. An additional goal was to find an index (based on eye movements, mouse tracking, and time) that would be assessing the effectiveness of the GUI layout on both devices. The main motivation for conducting the research described in the article was the desire to find a synthetic measure based on more than one factor (time) in the context of determining the effectiveness of the GUI.
Designing Immersive Virtual Environments for Human Behavior Research

What are strategies for the design of immersive virtual environments (IVEs) to understand environments’ influence on behaviors? To answer this question, we conducted a systematic review to assess peer-reviewed publications and conference proceedings on experimental and proof-of-concept studies that described the design, manipulation, and setup of the IVEs to examine behaviors influenced by the environment. Eighteen articles met the inclusion criteria. Our review identified key categories and proposed strategies in the following areas for consideration when deciding on the level of detail that should be included when prototyping IVEs for human behavior research: 1) the appropriate level of detail (primarily visual) in the environment: important commonly found environmental accessories, realistic textures, computational costs associated with increased details, and minimizing unnecessary details, 2) context: contextual element, cues, and animation social interactions, 3) social cues: including computer-controlled agent-avatars when necessary and animating social interactions, 4) self-avatars, navigation concerns, and changes in participants’ head directions, and 5) nonvisual sensory information: haptic feedback, audio, and olfactory cues.
Improving Editorial Workflow and Metadata Quality at Springer Nature

Identifying the research topics that best describe the scope of a scientific publication is a crucial task for editors, in particular because the quality of these annotations determine how effectively users are able to discover the right content in online libraries. For this reason, Springer Nature, the world's largest academic book publisher, has traditionally entrusted this task to their most expert editors. These editors manually analyse all new books, possibly including hundreds of chapters, and produce a list of the most relevant topics. Hence, this process has traditionally been very expensive, time-consuming, and confined to a few senior editors. For these reasons, back in 2016 we developed Smart Topic Miner (STM), an ontology-driven application that assists the Springer Nature editorial team in annotating the volumes of all books covering conference proceedings in Computer Science. Since then STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, for a total of about 800 volumes per year. Over the past three years the initial prototype has iteratively evolved in response to feedback from the users and evolving requirements. In this paper we present the most recent version of the tool and describe the evolution of the system over the years, the key lessons learnt, and the impact on the Springer Nature workflow. In particular, our solution has drastically reduced the time needed to annotate proceedings and significantly improved their discoverability, resulting in 9.3 million additional downloads. We also present a user study involving 9 editors, which yielded excellent results in term of usability, and report an evaluation of the new topic classifier used by STM, which outperforms previous versions in recall and F-measure.
Sustainable Development of College and University Education by use of Data Mining Methods

To improve the education efficiency of the students, the student-centered education plan is explored. First, the Apriori algorithm of association rules is used to mine the potential related patterns in the score data of college students and establish a reasonable teaching method. Second, aided by the decision tree model, the factors affecting students' academic performance are studied, and the potential relationship between different courses is studied. Finally, the Apriori algorithm of association rules combined with decision tree model is used to generate the early warning mechanism of students' achievement, and the course performance of college students is empirically analyzed. The results show that: C language has two sides of dependence on many subjects; higher mathematics → linear algebra → mathematical statistics → computer composition principle → computer network. The teaching scheme of C language → C + + → Java more conforms to the learning mechanism of college students. Through empirical analysis, the early warning mechanism of association rule Apriori algorithm and decision tree model can effectively analyze student's course and give student's achievement. It is found that the method proposed can provide theoretical basis for students, teachers, and university administrators to carry out education reform and education management decision-making, improve students' performance and education quality, and realize the "student-oriented" education concept, so it can be applied to the actual education management.
Entity Relation Extraction Based on Entity Indicators

Relation extraction aims to extract semantic relationships between two specified named entities in a sentence. Because a sentence often contains several named entity pairs, a neural network is easily bewildered when learning a relation representation without position and semantic information about the considered entity pair. In this paper, instead of learning an abstract representation from raw inputs, task-related entity indicators are designed to enable a deep neural network to concentrate on the task-relevant information. By implanting entity indicators into a relation instance, the neural network is effective for encoding syntactic and semantic information about a relation instance. Organized, structured and unified entity indicators can make the similarity between sentences that possess the same or similar entity pair and the internal symmetry of one sentence more obviously. In the experiment, a systemic analysis was conducted to evaluate the impact of entity indicators on relation extraction. This method has achieved state-of-the-art performance, exceeding the compared methods by more than 3.7%, 5.0% and 11.2% in F1 score on the ACE Chinese corpus, ACE English corpus and Chinese literature text corpus, respectively.
The Weighted Values of Solar Evaporation’s Environment Factors Obtained by Machine Learning

Enhancing the efficiency of solar still is important for solar stills.In this study, the weighted values of environment factors (descriptors) on the efficiency of solar evaporation are obtained by using a machine learning algorithm, random forest.To verify the advancement between random forest and mathematical data analysis, two traditional methods, pair wise plots and Pearson correlation analysis, are conducted for comparison.Experimental data are obtained from around 100 articles since 2014.The results indicated that traditional methods failed at obtaining reasonable weighted values, while random forest is competent.It is found that thermal design is the most significant descriptors to obtain a high efficiency.The lack of complete dataset is the main challenge for more in-depth and comprehensive analysis.This work may promote the enhancement of production and efficiency of solar stills.
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling

Video super-resolution (VSR) aims at restoring a video in low-resolution (LR) and improving it to higher-resolution (HR). Due to the characteristics of video tasks, it is very important that motion information among frames should be well concerned, summarized and utilized for guidance in a VSR algorithm. Especially, when a video contains large motion, conventional methods easily bring incoherent results or artifacts. In this paper, we propose a novel deep neural network with Dual Subnet and Multi-stage Communicated Upsampling (DSMC) for super-resolution of videos with large motion. We design a new module named U-shaped residual dense network with 3D convolution (U3D-RDN) for fine implicit motion estimation and motion compensation (MEMC) as well as coarse spatial feature extraction. And we present a new Multi-Stage Communicated Upsampling (MSCU) module to make full use of the intermediate results of upsampling for guiding the VSR. Moreover, a novel dual subnet is devised to aid the training of our DSMC, whose dual loss helps to reduce the solution space as well as enhance the generalization ability. Our experimental results confirm that our method achieves superior performance on videos with large motion compared to state-of-the-art methods.
Generating and detecting entangled cat states in dissipatively coupled degenerate optical parametric oscillators

Non-Gaussian continuous-variable states play a central role both in the foundations of quantum theory and for emergent quantum technologies. In particular, ``cat states,'' i.e., two-component macroscopic quantum superpositions, embody quantum coherence in an accessible way and can be harnessed for fundamental tests and quantum information tasks alike. Degenerate optical parametric oscillators can naturally produce single-mode cat states and thus represent a promising platform for their realization and harnessing. We show that a dissipative coupling between degenerate optical parametric oscillators extends this to two-mode entangled cat states, i.e., two-mode entangled cat states are naturally produced under such dissipative coupling. While overcoming single-photon loss still represents a major challenge towards the realization of sufficiently pure single-mode cat states in degenerate optical parametric oscillators, we show that the generation of two-mode entangled cat states under such dissipative coupling can then be achieved without additional hurdles. We numerically explore the parameter regime for the successful generation of transient two-mode entangled cat states in two dissipatively coupled degenerate optical parametric oscillators. To certify the cat-state entanglement, we employ a tailored, variance-based entanglement criterion, which can robustly detect cat-state entanglement under realistic conditions.
A large English–Thai parallel corpus from the web and machine-generated text

The primary objective of our work is to build a large-scale English–Thai dataset for training neural machine translation models. We construct scb-mt-en-th-2020, an English–Thai machine translation dataset with over 1 million segment pairs, curated from various sources: news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data, government documents, and text artificially generated by a pretrained language model. We present the methods for gathering data, aligning texts, and removing preprocessing noise and translation errors automatically. We also train machine translation models based on this dataset to assess the quality of the corpus. Our models perform comparably to Google Translation API (as of May 2020) for Thai–English and outperform Google when the Open Parallel Corpus (OPUS) is included in the training data for both Thai–English and English–Thai translation. The dataset is available for public use under CC-BY-SA 4.0 License. The pre-trained models and source code to reproduce our work are available under Apache-2.0 License.
PCCM-GAN: Photographic Text-to-Image Generation with Pyramid Contrastive Consistency Model

Synthesizing photographic images from given text descriptions is a challenging problem. Although previous many studies have made significant progress on the visual quality of the generated images by using the multi-stage and attentional network, they ignore the interrelationships between the images generated by the generator in each stage and simply leverage the attention mechanism. In this paper, the Photographic Text-to-Image Generation with Pyramid Contrastive Consistency Model (PCCM-GAN) is proposed to generate photographic images. PCCM-GAN introduces two modules: a Pyramid Contrastive Consistency Model (PCCM) and a stack attention model (Stack-Attn). Based on generated images from the different stages, PCCM is proposed to compute a contrastive loss for training the generator. Stack-Attn concentrates on generating images with more details and better semantic consistency by stacking the global–local attention mechanism. And visual inspection of the inner product of PCCM and Stack-Attn is also performed to validate their effectiveness. Extensive experiments and ablation studies on the CUB and MS-COCO datasets prove the superiority of the proposed method.