Reviewerly/exHarmony huggingface dataset:
Photon counting statistics with imperfect detectors

Abstract Measurement of photon statistics is an important tool for the verification of quantum properties of light. Due to the various imperfections of real single photon detectors, the observed statistics of photon counts deviates from the underlying statistics of photons. Here we analyze statistical properties of coherent states, and investigate a connection between Poissonian distribution of photons and sub-Poissonian distribution of photon counts due to the detector dead-time corrections. We derive a functional dependence between the mean number of photons and the mean number of photon counts, as well as connection between higher-order statistical moments, for the pulsed or continuous wave coherent light sources, and confirm the results by numerical simulations.
The Computational Surfacing of Memories: Promoting the Memorable

This chapter focuses on the processes of ranking that are a central part of the automatic production of memory. It shows how the taxonomisation of people’s memories provided not only the conditions by which to determine what counts as a memory and what does not, but also the conditions that enabled their ranking. This was the basis for how memories could be variously weighted and resurfaced within the throwback feature. As such, the processes of classification informed Facebook’s ranking algorithm, which afforded the targeting of certain memories to certain users at certain times. The chapter argues that the automatic production of memories is not only underpinned by the partitioning of the memorable, but also <italic>the promotion of the memorable.</italic> In this framework, rankings participate in ontologically promoting users’ past content into ‘memories’. As such, the promotion of content into memories actively generates novel kinds of encounters between the past and present, between individual users, and between users and the platform.
Product Recommendation and Context Validation Based E-Commerce System With Currency Free Economy

Due to the covid-9 situation, online shopping shows rapid growth among Sri Lanka and other countries. Meanwhile, with the visible downward trend of the Sri Lankan economy, people have been suffering due to inflation, leading to higher expenses of goods and services. A web-based solution called ‘Ceylon Barter Bay’ was developed as an e-bartering platform for Sri Lankans to get bartering experience and develop one-to-one trading. This paper comes with an appropriate business model for ‘Ceylon Barter Bay’ as a novice entrepreneur idea. This website was developed with enhanced abuse detectors and a related product recommendation system. Natural language processing and machine learning techniques are used in the process to get a better solution. Since the developed system is mainly based on advertising, a random forest algorithm-based machine learning model with 99% accuracy detects the context offensiveness. To detect violent behavior in feedback/comments, the logistic regression algorithm-based machine learning model was used with 88% accuracy. ‘Ceylon Barter Bay’ will recommend related items. Both collaborative and content-based recommendations have been performed using linear regression, respectively. In Sri Lanka, this has been recognized as an acceptable solution to break the monopoly of money via a web-based application
A Particle Swarm Optimization Based Feature Selection Approach for Multi-source Visual Domain Adaptation

Labeled data plays a pivotal role in training primitive machine learning models to classify the target domain information. But acquiring the necessary amount of labeled data is not always possible. Hence Domain adaptation utilizes the existing knowledge from a related source domain for training the target domain classifier. It can be possible that one or more related source domains can even be deployed for training the model. By taking multiple sources into account, we can have a massive database for the training purpose. The concept of multi-source domain adaptation makes use of related multiple source domains. Existing multi-source methods do not consider the problem of distorted features, which can lead to a decline in the overall accuracy of the model. To overcome this issue, we have proposed a novel Particle Swarm Optimization based Feature selection approach for multi-source domain adaptation (PSO-MDA) which provides an optimal set of features that ultimately enhances the performance of the target domain classifier. PSO-MDA utilizes the fitness function that consists of different relevant objectives for minimization of distribution differences between multiple source domains and the target domain. We have considered several multi-source scenarios like double-source, triple-source, and quadruple-source domain adaptation scenarios for two publicly available real-world datasets. Comprehensive experiments on different multi-source scenarios have proved the efficacy of our proposed PSO-MDA over existing approaches.
Learning Attentive Cooperation in Multi-agent Reinforcement Learning with Graph Convolutional Network

Cooperative multi-agent reinforcement learning (MARL) is a key tool for addressing many real-world problems and is becoming a growing concern due to communication constraints during execution and partial observation. In prior works, the popular paradigm of centralized training with decentralized execution (CTDE) is used to mitigate this problem. However, the intensity of the cooperative relationship is not paid too much attention. In this paper, we propose an Attentive Cooperative MARL framework based on the Neighborhood Graph Convolutional Network (AttCoop-Q) to help agents communicate with each other and generate cooperative features for attentive cooperative policy learning. AttCoop-Q consists of the neighborhood graph convolutional network (NGCN) module and the attentive cooperation policy learning module. NGCN encodes the current situation, constructs a neighboring agent graph and uses the architecture of Neighborhood Graph Convolutional Network (GCN) to extracts cooperative features. The attentive cooperation policy learning module generates weight vectors using cooperative features to generate adapted Q-values which are further used to learn the total Q value. Experimental results on challenging multi-agent StarCraft benchmark tasks show that our proposed method greatly boosts the performance compared with other popular MARL approaches.
Method To Detect the Vibration State Based on the Shifting Distance of Characteristic Frequency

Vibration frequency detection of objects is very important in the field of industrial automation, such as the forward and backward oscillation frequency of wind turbine blades when working. Millimeter-wave FMCW radar can make up for some shortcomings of conventional sensors, so it can be introduced into the field of vibration state detection. In this paper, a new method of mechanical vibration state based on distance classification rule in pattern recognition is proposed. The motion state can be identified by measuring the vibration response of a fixed point. Therefore, it is possible to determine whether the vibration state is faulty according to the shifting distance of the characteristic frequency in the signal, and this paper uses the movement of the pendulum to simulate the vibration of the fixed point, and carry out the simulation calculation when the millimeter wave FMCW radar is used to detect the vibration point of the pendulum, which verifies the feasibility of the method.
Optical Training Framework for Optical Diffractive Deep Neural Network via Direct Feedback Alignment

We propose a novel optical training framework for Optical Diffractive Deep Neural Network based on Direct Feedback Alignment. The proposed method can accelerate the training process enormously without significant loss of accuracy.
Research on the intelligent teaching mode of Russian in Colleges and Universities Based on computer

Today's employment prospects of small languages are relatively good. This is also the reason for the sharp increase in the number of small language learners in domestic schools. Russian learning has always been concerned by people. Nowadays, some enterprises in Chinese society have a great demand for Russian talents. Colleges and universities also formally implement the new mode of Russian talent training. Russian learning mode based on intelligent teaching has been developed. This paper describes the main problems of traditional Russian teaching. Finally, this paper puts forward the theory of computer Russian teaching mode reform.
Publishing Your Game

You finally did it! You created your very first 2D platformer in Godot! You’ve learned so much, including animating the character, designing the game world using TileMaps, adding enemies that can hurt the player, and adding different coins that the player can collect. We created a title screen and a heads-up display (HUD) for our game that calculated and displayed the total score as well as the number of lives the player has left. We also made the game even more fun to play by adding music and cool sound effects!
Phase-Based Signal Representations for Scattering

The scattering transform is a non-linear signal representation method based on cascaded wavelet transform magnitudes. In this paper we introduce phase scattering, a novel approach where we use phase derivatives in a scattering procedure. We first revisit phase-related concepts for representing time-frequency information of audio signals, in particular, the partial derivatives of the phase in the time-frequency domain. By putting analytical and numerical results in a new light, we set the basis to extend the phase-based representations to higher orders by means of a scattering transform, which leads to well localized signal representations of large-scale structures. All the ideas are introduced in a general way and then applied using the STFT.
Types

A set is a collection of things. In programming, a type is also a collection of things.Types are used in logic and mathematics with a similar meaning but without the computational constraints imposed by their use in programming, see https://en.wikipedia.org/wiki/Type_theory.
Meet the skies of the world first intercontinental-experimental course of teacher training in astronomy, of a cooperative and participatory type, in a time of physical distancing

Abstract For over 30 years, the MCE Sky Pedagogy Research Group (Italy) and Plaza del Cielo Complex (Argentina) had been offering teachers and educators many projects and activities related to training and teaching/learning processes having the sky and the study of the multiple relationships of humans with the sky as a focus of research. We have developed didactical methods based on direct experience, naked-eye activities, cooperation and exchange of experiences. The pandemic forced us to suspend the in-person meetings, which are at the center of our research in the Teaching of Astronomy, so we decided to react constructively exploring virtuality designing the course “Meet the skies of the world”. Two courses where developed from June to September 2020, each one having 4 virtual meetings of 2,5 hours each, with more than a hundred participants from different countries. A Level II course will be developed during the first semester of 2021.
Multi-Stream Feature Refinement Network for Human Object Interaction Detection

Human-Object Interaction (HOI) detection is a crucial problem for comprehensive visual understanding. Many existing methods often exploit to integrate multiple level features into a framework to infer the interactions. However, most methods simply concatenate all these features that are not explicitly embedded in the feature learning for HOI detection. In this paper, we are trying to fuse these features explicitly using a multi-stream feature refinement network. The network extracts the visual features of humans, contexts, and objects, which receives the attentions from human poses, spatial configurations, and semantic prior knowledge of objects to refine these visual features, respectively. We verify our method on V-COCO and HICO-DET datasets with extensive experiments. The experimental results demonstrate that our method is a simple yet effective for HOI detection, achieving superior performance to those state-of-the-art methods.
Single Input Stereo HDR Imaging using Deep Learning

In the domain of stereo vision, Stereo High Dynamic Range Imaging (SHDRI) is a revolutionary concept. Traditionally, monocular depth estimators had to be trained with ground truth and binocular stereo data. With the use of deep learning, this paper suggests a new SHDRI approach that uses a single image. The Left View (LV) which is the input, is given to a pretrained monocular depth estimator, which uses depth hints obtained from off-the-shelf stereo algorithms. The estimator provides us with the LV’s depth map as well as the disparity values which are then combined with the input to create the Right View (RV), resulting in the stereo pair. Using Power Law Transformation, we make multiple exposed images of both the LV and the RV. A unified and unsupervised end-to-end exposure fusion network fuses the LV and RV exposures and generates HDR images for both the views, forming the stereo HDR image pair. Additionally, we produce anaglyphs from the resulting HDR image pair, which can be employed to intuitively perceive three-dimensional structures.
Research on the Optimal Methods for Graph Edit Distance

Graph edit distance is an important way to measure the similarity of pairwise graphs and has been widely used to bioinformatics, chemistry, social networks, etc. However, the expensive computation of graph edit distance poses serious algorithmic challenges. One of recent methodologies to obtain graph edit distance is to search the vertex mapping. In existing methods, A-Star heuristic search and pruning are used to improve the performance, but they still suffer huge temporal-spatial consumption and loose lower bound. In this paper, based on the heuristic A-Star search methods, three optimal methods are proposed to improve the mapping search strategy. First, a pruning strategy based on Symmertry-Breaking is proposed which defines the concept of mapping-equivalence, and prunes before the equivalence mappings are extended. Second, a pruning strategy based on upper bound is proposed to filter invalid mappings in the priority queue to speed up the search time, which uses Hungarial algorithm to obtain the upper bound. Third, the dequeued order is specified for the mappings in the priority queue with the same lower bound of the edit cost. Experiments on real datasets show that our methods have significant temporal-spatial optimal results
Security Awareness in the Internet of Everything

Our societal infrastructure is transforming into a connected cyber-physical system of systems, providing numerous opportunities and new capabilities, yet also posing new and reinforced risks that require explicit consideration. This chapter addresses risks specifically related to cyber-security. One contributing factor, often neglected, is the level of security education of the users. Another factor, often overlooked, concerns security-awareness of the engineers developing cyber-physical systems. Authors present results of interviews with developers and surveys showing that increase in security-awareness and understanding of security risks, evaluated as low, are the first steps to mitigate the risks. Authors also conducted practical evaluation investigating system connectivity and vulnerabilities in complex multi-step attack scenarios. This chapter advocates that security awareness of users and developers is the foundation to deployment of interconnected system of systems, and provides recommendations for steps forward highlighting the roles of people, organizations and authorities.
A Novel Unscented Particle Filter Based on Maximum Fuzzy Correntropy

In order to solve the instability of the traditional particle filter algorithm, a maximum fuzzy correntropy unscented particle filter algorithm (MFC-UPF) is proposed. The algorithm constructs the importance density function based on the results of the maximum fuzzy correntropy unscented filtering algorithm, which can improve the ability to resist non-Gaussian noise interference and effectively take advantage of the latest measurement information, the residual resampling method is used to further improve the filtering accuracy and stability. The simulation results show that the filtering performance of the algorithm is stable, which greatly improves the influence caused by noise fluctuations, and the proposed MFC-UPF algorithm can track the bearings-only maneuvering target accurately, and the tracking performance is better than the unscented Kalman filter (UKF), the maximum correntropy unscented Kalman filter (MCUF) and maximum fuzzy correntropy unscented Kalman filter (MFC-UF).
EPINE: Enhanced Proximity Information Network Embedding

In this work, we focus on improving high-order proximity of unsupervised homogeneous network embedding (NE). NE is proposed to represents every vertex of networks into a low-dimensional vector and meanwhile preserves network information as most as possible. Adjacency matrices retain most of the information in network data, and directly characterize the first-order proximity. In consideration of structure information in networks, we devote ourselves to mining valuable information in adjacency matrices at a deeper level. Under the objective, many NE methods proposed to calculate high-order proximity by the powers of adjacency matrices, which is not well- designed enough as they may contain redundancy information. To further explore high-order proximity, we propose to redefine high-order proximity in a more intuitive way. Specifically, we calculate the strength of high-order proximity by shortest paths between nodes and weights along with the paths, which is closely related to the edge weights. Consequently, for unweighted networks, we evaluate edge weights based on endpoint degrees. In comprehensive experiments on real-world network datasets, our method outperforms other competitive methods in downstream machine learning tasks such as network reconstruction and node classification.
An Experimental Study to Determine the Optimum Order of Design Review Parameters for Designing Review in Immersive Virtual Environment

This paper studies the experiential learning in a virtual environment using HMD-based immersive virtual reality technologies. This digital technology is mostly beneficial in product design and development. However, usability of virtual reality in the area of the design is still explorative for reviewing a 3D model. Several factors having an impact on the design review of 3D models in the virtual environment. In experiment, design review score is calculated for different working conditions with different parameter ranges. The significant order of the parameters is determined using Taguchi method, and the outcomes are compared with ANOVA method. Experimental results indicate that the effect of factor-1 (grooves) is more contributing than factor-3 (blocks), and factor-2 (sidewall) is least significant on design review process in simulated 3D state.
On Some Categories Underlying Knowledge Graphs

Krídlo, Ondrej Ojeda-Aciego, Manuel Put, Tim Reformat, Marek Z.This paper proposes a method to provide a categorical structure for RDF-based data representing descriptions of entities. This is the first step towards our aim to further develop the underlying categorical structure so that we can eventually provide an internal logic which enables us to focus on analysis of properties of entities.
Design of Belarusian and Russian natural language interfaces for online help systems

O b j e c t i v e s. The main goal of the work is a research of the natural language user interfaces and the development of a prototype of such an interface. The prototype is a bilingual Russian and Belarusian question-and-answer dialogue system. The research of the natural language interfaces was conducted in terms of the use of natural language for interaction between a user and a computer system. The main problems here are the ambiguity of natural language and the difficulties in the design of natural language interfaces that meet user expectations. M e t ho d s. The main principles of modelling the natural language user interfaces are considered. As an intelligent system, it consists of a database, knowledge machine and a user interface. Speech recognition and speech synthesis components make natural language interfaces more convenient from the point of view of usability. R e s u l t s. The description of the prototype of a natural language interface for a question-and-answer intelligent system is presented. The model of the prototype includes speech-to-text and text-to-speech Belarusian and Russian subsystems, generation of responses in the form of the natural language and formal text. An additional component is natural Belarusian and Russian voice input. Some of the data, required for human voice recognition, are stored as knowledge in the knowledge base or created on the basis of existing knowledge. Another important component is Belarusian and Russian voice output. This component is the top required for making the natural language interface more user-friendly. Co n c l u s i o n. The article presents the research of natural language user interfaces, the result of which provides the development and description of the prototype of the natural language interface for the intelligent question- and-answer system.
Bounding Means of Discrete Distributions

We introduce methods to bound the mean of a discrete distribution (or finite population) based on sample data, for random variables with a known set of possible values. In particular, the methods can be applied to categorical data with known category-based values. For small sample sizes, we show how to leverage the knowledge of the set of possible values to compute bounds that are stronger than for general random variables such as standard concentration inequalities.
Pulsed Signal Detection Utilizing Wavelet Analysis with a Deep Learning Approach

This paper explores the use of wavelet analysis and deep learning techniques to classify pulsed binary data over a severely noisy communications channel. Military communications need to operate in extremely harsh radio environments, which may include hostile intent to disrupt communications. Consequently, unconventional methods, such as pulsed communications, need to be investigated. Pulsed transmission techniques utilized for such channels typically result in pulses that are not easily discerned from noise and other interference. Deep learning techniques have proven advantageous in quickly and efficiently identifying minute variations in large data sets. This paper presents methods for leveraging deep learning techniques for pulsed signal detection.