Reviewerly/exHarmony huggingface dataset:
Vietnamese Text’s Writing Styles Based Authorship Identification Model

Identification of authorship is a research topic in natural language processing that has been interesting in recent years. Previously, texts were studied through a large variety of feature extraction methods to identify the author of the content. Advanced approaches based on deep learning have recently been applied to authorship attribution. This paper introduces a new model called ViBert4Author (V4A), a fine-tuning version of the pre-trained PhoBERT language model with the addition of dense layer and soft-max through combining the same algorithms. The feature extraction method is used for author classification in Vietnamese literature. In addition, our article also introduces a dataset that has been collected based on self-developed tools, the dataset on building over 800 works from 8 authors named VN-Literature. We also performed many tests on English datasets to evaluate the model: blogs, emails published on Kaggle, and pre-trained multi-languages for testing. We give a comprehensive analysis of the advantages and disadvantages of the proposed method. In addition, we evaluate the extraction of additional features (stylometric and hybrid features) in our assessment of approaches using the F1-score measure. The results show that our proposed model has improved performance over previous methods, in which the model that combines stylistic features and modern methods achieves outstanding performance.
Single Photon on a Beam Splitter

The beam splitter is an important optical element in quantum optics experiments. The classical and quantum treatment of the beam splitter is presented. We derive the photodetection probabilities for a single photon on a beam splitter, including single photon detection and double photon detection (coincidence counting). The correlation function is introduced for classical and quantum light. We show that the beam splitter creates an entangled state from a single photon input. The Hanbury Brown–Twiss experiment is introduced for characterizing light sources.
GUILD - A Generator for Usable Images in Large-Scale Datasets

Large image datasets are important for many different aspects of computer vision. However, creating datasets containing thousands or millions of labeled images is time consuming. Instead of manual collection of a large dataset, we propose a framework for generating large-scale datasets synthetically. Our framework is capable of generating realistic looking images with varying environmental conditions, while automatically creating labels. To evaluate usefulness of such a dataset, we generate two datasets containing vehicle images. Afterwards, we use these images to train a neural network. We then compare detection accuracy to the same neural network trained with images of existing datasets. The experiments show that our generated datasets are well-suited to train neural networks and achieve comparable accuracy to existing datasets containing real photographs, while they are much faster to create.
GAMING DEPENDENCE AS A SUBJECT OF RESEARCH

This article is devoted to the philosophical analysis of the formation and development of games as a social phenomenon. The game is the most necessary part of the universal and national culture, the basis of the ethical and aesthetic development of objective reality. In this regard, all the peoples of the world have generally accepted norms for the prohibition of gambling, in particular, the religions of the East consider it as a serious problem in the perfection of the individual. Thus, the author draws attention to online computer games among young people, the popularity of which is growing dynamically, which requires the search for new solutions from the destruction of the social foundations of public life.
A Session-Based Recommender System for Learning Resources

Learning resource recommendation systems can help learners find suitable resources (e.g., books, journals, …) for learning and research. In particular, in the context of online learning due to the impact of the COVID-19 pandemic, the learning resource recommendation is very necessary. In this study, we propose using session-based recommendation systems to suggest the learning resources to the learners. Experiments are performed on a learning resource dataset collected at a local university and a public dataset. After preprocessing the data to convert it to session form, the Neural Attentive Session-based Recommendation (NARM) and Recurrent Neural Networks (GRU4Rec) models were used for training, testing, and comparison. The results show that recommending learning resources according to the NARM model is more effective than that of the GRU4Rec model, and thus, using the session-based recommendation system would be a promising approach for learning resource recommendation.
Geochemical properties and heavy metal contents of carbonaceous rocks in the Pliocene siliciclastic rock sequence from southeastern Denizli-Turkey

Abstract Pliocene-aged carbonaceous rocks of varying thicknesses, alternating with siliciclastic and carbonate rock units, are located on the northern border of the Çameli-Acıpayam basin (in southwestern Turkey), which is filled with thick Neogene sediments. Organic and inorganic geochemical properties of carbonaceous rocks were examined to evaluate their hydrocarbon production potential and paleo-depositional conditions (provenance, paleoclimate, paleosalinity, paleoredox, weathering, heavy metal contents, etc.). Based on organic geochemical analysis, carbonaceous rocks show good to excellent source rock potential and have gas production potential. Organic substances are mostly of allochthonous origin, and their thermal maturity degree is quite low. Carbonaceous rocks have a high amount of inorganic substance (minerals) composed of quartz, clay + mica minerals (mostly illite and a lesser amount of smectite, chlorite, and kaolinite), feldspar, and opaque minerals (pyrite and ilmenite). Although carbonaceous rocks have similar major oxide and trace element concentrations, they show clear differences in terms of Fe 2 O 3 , MgO, and some heavy metals (such as Ni, Cr, Co, V, and Sc). The major oxide, trace element concentrations, various element ratios of carbonaceous rocks bear the characteristics of mafic/ultramafic magmatic rocks formed on the active continental margin. In addition, carbonaceous rocks were precipitated in a brackish water environment under oxic conditions. The sediments, which were rapidly stored without recycling in the deposition environment where hot/humid climatic conditions are effective, did not undergo intense decomposition in the source area.
Three Dimensional Path Planning of Snake-Arm Robot Based on Improved Ant Colony Algorithm

A path planning algorithm based on an improved ant colony algorithm was proposed to solve the path planning problem of snake-arm robots in a structured environment. The heuristic function of the ant colony algorithm is combined with an artificial potential field. Setting up a repulsive force field around the obstacle and an attractive field at the target point, the snake-arm robot is guided to advance from the starting point to the target point while avoiding the obstacle. Chaos disturbance is added to the pheromone update to improve the global search capability of the algorithm. In path optimization, the linear generation algorithm and cubic uniform B-spline curve interpolation algorithm are used to optimize the path globally.
Introduction

Three-valued logics are traditional and have been studied in variant ways ([4–7, 18]).
Blockchain Peer-to-Peer Network: Performance and Security

Mistrusting nodes in a blockchain can reach consensus without the need of a trusted central entity. Instead, the nodes reach consensus through exchanging information on a peer-to-peer (P2P) network, without pre-established identities. Serving as the foundation of the blockchain, the P2P network plays critical roles in all performance and security aspects of the blockchain system. While P2P networks had been previously examined for many applications domains, including the file sharing systems, there is relatively less understanding on blockchain P2P networks that differs substantially from traditional P2P systems. In this chapter, we will cover different aspects of blockchain P2P networks from topology, peer discovery, known attacks, and defenses to improvement proposals to increase the throughput and reduce the latency in blockchain. Finally, we investigate theoretical limit on the throughput of blockchain systems in which nodes have heterogeneous capacities. We provide insights and discussion on how to construct a network to achieve the maximum theoretical limit in throughput.
PH-Remix. Enhancing Cataloguing and Promotion of Film Heritage Through Video Remix and Artificial Intelligence

PH-Remix is a prototype of a web platform based on artificial intelligence that enables the uploading, cataloguing, search, consultation, extraction and remix of multimedia content. The platform was developed with the aim of creating an innovative tool to complement the classic methodologies of research and study of sources by providing easy access and the possibility to reuse a large number of primary filmic sources. Paying particular attention to the challenges and critical issues that may arise during the development of such a tool, this paper aims to present: - The remix prototype and some possible practical applications in the context of public history arising from the possibility of creating new contributions/multimedia contents; - The machine learning techniques used to extract information from audiovisual works.
Big Data Analysis for Anti-Money Laundering: A Case of Open Source Greenplum Application

For the anti-money laundering regulations of financial institutions, it is important to do the timely big data analysis to continuously optimize the comprehensiveness, correctness and effectiveness of transaction monitoring models. And at the same time, how to reduce the cost investment for the solution is also important. The traditional solutions are usually based on Hadoop family, which is costly both in technical stack building and in code programming. And sometimes, although these costly solutions can be borne, the response time cannot be borne. This paper is an application case to build the solution on open source distributed database Greenplum. By utilizing the share-nothing MPP structure and the software ecosystem of Greenplum, a solution of high efficient big data analysis through large scale parallel computing is designed. After ETL processing by the tools such as gpfdist, we use advanced SQL features to support the calculation of several key transaction monitoring statistical indicators for anti-money laundering. The test cases show that, through this way, we can not only reduce the cost in technical stack building and in code programming, but also get the analysis results in just few seconds. Thus, this is a feasible solution for the similar big data analysis tasks.
MSDNet: Multi-scale Dense Networks for Salient Object Detection

Since the fully convolutional networks was proposed, it has made great progress in the salient object detection. However, this kind of network structure still has obvious problems of incomplete salient objects segmentation and redundant information. Therefore, this paper propose a novel network model named Multi-scale Dense Network (MSDNet) to solve the above problems. First, we designed a multi-receptive enhancement and supplementation module(MRES), which increases the discriminability of features through feature interaction under different receptive fields. Second, we design a network framework MSDNet that first uses dense feature interactions and a pyramid-shaped feature fusion structure to get enhanced features and better features fusion. Experimental results on five benchmark datasets demonstrate the proposed method against 11 state-of-the-art approaches, it can effectively improve the completeness of salient objects and suppress background information.
A Pragmatic Label-Specific Backdoor Attack

Backdoor attacks, as an insidious security threat to deep neural networks (DNNs), are adept at injecting triggers into DNNs. A malicious attacker can create a link between the customized trigger and the targeted label, such that the prediction of the poisoned model will be manipulated if the input contains the predetermined trigger. However, most existing backdoor attacks define an obvious trigger (eg: conspicuous pigment block) and need to modify the poisoned images’ label, causing these images seems to be labeled incorrectly, which leads to these images can not pass human inspection. In addition, the design of the trigger always needs the information of the entire training data set, an extremely stringent experiment setting. These settings above remarkably restrict the practicality of backdoor attacks in the real world. In our paper, the proposed algorithm effectively solves these restrictions of existing backdoor attack. Our Label-Specific backdoor attack can design a unique trigger for each label, while just accessing the images of the target label. The victim model trained on our poisoned training dataset will maliciously output attacker-manipulated predictions while the poisoned model is activated by the trigger. Meanwhile victim model still maintains a good performance confronting benign data samples. Hence, our proposed backdoor attack approach must be more practical.
Research on the Recognition of Internet Buzzword Features Based on Transformer

Abstract Accurate identification of Internet buzzwords plays an important role in positive Internet opinion guidance. A Transformer-based Internet buzzword feature recognition system was designed to address this problem. The traditional way of crawling data has been improved, a real-time crawling module has been added, and an Internet buzzword corpus has been constructed by itself. The traditional way of crawling data has been improved, a real-time crawling module has been added, and an Internet buzzword corpus has been constructed by itself. Traditional machine learning models suffer from gradient disappearance and gradient explosion, the Transformer model, with its parallel computing and self-attentive mechanism, is a good solution to these problems, and its bi-directional connection allows the parameters of the context to be updated uniformly, thus allowing better aggregation of information and solving the problem of scattered contextual information. Transformation of the position-encoded part of the Transformer model starts with a relative position representation (RPR). It compensates for its inability to obtain relative location information. The experimental results show that the improved Transformer model can achieve an accuracy rate of 90.1%, a recall rate of 92.13%, and an F1 value of 91.16% in recognizing Internet buzzwords.
Bayesian Analysis of Longitudinal Ordinal Data Using Non-Identifiable Multivariate Probit Models

Multivariate probit models have been explored for analyzing longitudinal ordinal data.However, the inherent identification issue in multivariate probit models requires the covariance matrix of the underlying latent multivariate normal variables to be a correlation matrix and thus hinders the development of efficient Bayesian sampling methods.It is known that non-identifiable models may produce Markov Chain Monte Carlo (MCMC) samplers with better convergence and mixing than identifiable models.Therefore, we were motivated to construct a non-identifiable multivariate probit model and to develop efficient MCMC sampling algorithms.In comparison with the MCMC sampling algorithm based on the identifiable multivariate probit model, which requires a Metropolis-Hastings (MH) algorithm for sampling a correlation matrix, our proposed MCMC sampling algorithms based on the non-identifiable model circumvent an MH algorithm by a Gibbs sampler for sampling a covariance matrix and thus accelerate the MCMC convergence.We illustrate our proposed methods using simulation studies and two real data applications.Both the simulation studies and the real data applications show that constructing nonidentifiable models may improve the convergence of the MCMC algorithms compared with the identifiable models.The marginalization of the redundant parameters in the non-identifiable models should be considered in developing efficient MCMC sampling algorithms.This investigation shows that construction of non-identifiable models is valuable in developing MCMC sampling methods and illustrates advantages and disadvantages of construction of non-identifiable models to improve the convergence of the MCMC sampling components.
Combining User Inherent and Contextual Preferences for Online Recommendation in Location-Based Services

With the development of mobile internet and smart devices, location-based services (LBS) have developed rapidly and attracted more and more users. The availability of a large amount of user interaction data makes it possible to provide more personalized and accurate recommendation services. However, in mobile scenarios, multiple influencing factors such as the diversity of user preferences, the variability of user behavior, and the dynamics of spatiotemporal contexts bring great challenges to recommendation services. To accurately capture the preferences of mobile users in dynamic contexts, we propose an Inherent and Contextual Preference-aware Attention Network (ICPAN) for online recommendation in location-based services. Our ICPAN consists of an inherent-preference mining module with self-attention layers, a contextual-preference perception module with improved IR $$^2$$ -tree-based index structures, and an online recommendation module. The inherent-preference and contextual-preference models are trained based on global historical behavior data and instantly selected context-sensitive data, respectively. And then the online recommendation module uses attention aggregation to couple the two preference representations to generate the final recommendation result. Extensive experiments are conducted on three real datasets, and the experimental results show that the proposed ICPAN outperforms existing state-of-the-art methods.
Intelligent Recommendation Method of Sports Tourism Route Based on Cyclic Neural Network

Due to the low matching degree between scenic spot characteristics and tourists’ interests, the accuracy of route recommendation is low. Therefore, an intelligent recommendation method of sports tourism route based on cyclic neural network is designed. On the premise of determining the recommendation target of sports tourism route, the characteristics of sports tourism attractions and routes and tourists’ interest are extracted. After clustering, the recommendation list is collaborative filtered from the perspective of tourists. Finally, the circular neural network is used to optimize the recommended route. The test results show that the MAE of the design method opinion results is basically within 0.1, which has high accuracy.
Video Quality Assessment of Danmaku-Based Video Saliency Regions

At present, objective quality assessment methods have significant advantages in video quality assessment (VQA), among which the objective assessment based on deep learning is superior in processing speed and accuracy. However, the objective assessment method has the limitation of poor interpretability of experimental results and lacks the participation of human thinking and experience. In this paper, we present a quality assessment method based on the danmaku visual saliency region. First, to obtain the sentiment trend of the danmaku, we conduct a sentiment analysis of the content of the danmaku. Then, to speed up the video quality assessment, we select the keyframes of the video according to the sentiment analysis. Finally, we innovatively use human visual characteristics to extract salient regions from video keyframes to make the results more subjective. The experiments show that the Pearson correlation coefficient (PCC) and root mean square error (RMSE) between the results of the proposed method and the subjective evaluation are improved, which conforms to the audience's subjective feelings about video viewing, and the assessment results are more accurate.
Analytical Estimation of the Energy Provided by the Battery-Based Photovoltaic Power Supply

Nowadays, surveillance and protection systems are standard equipment at many airports. However, their use can be quite restrictive in the case of their relatively short-term use in an area that does not normally extend to their perimeter of protection. The limiting factor in such situations is mainly their low mobility and dependence on external galvanic lines, especially if the individual sensors require mutually different power requirements. In consequence, this article is devoted to the analytical estimation of the energy provided by the photovoltaic power supply for a mobile monitoring unit, intended to monitor a surrounding area and inform about a possible unauthorized intrusion into perimeter protection. The essence of the analysis was to estimate the time of the one discharging cycle during which the designed battery-based photovoltaic system power supply can continuously provide sufficient energy to keep the mobile monitoring unit in its full operational mode.
SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning

Neural architecture search (NAS) has demonstrated amazing success in searching for efficient deep neural networks (DNNs) from a given supernet. In parallel, lottery ticket hypothesis has shown that DNNs contain small subnetworks that can be trained from scratch to achieve a comparable or even higher accuracy than the original DNNs. As such, it is currently a common practice to develop efficient DNNs via a pipeline of first search and then prune. Nevertheless, doing so often requires a tedious and costly process of search-train-prune-retrain and thus prohibitive computational cost. In this paper, we discover for the first time that both efficient DNNs and their lottery subnetworks (i.e., lottery tickets) can be directly identified from a supernet, which we term as SuperTickets, via a two-in-one training scheme with jointly architecture searching and parameter pruning. Moreover, we develop a progressive and unified SuperTickets identificationcesstab strategy that allows the connectivity of subnetworks to change during supernet training, achieving better accuracy and efficiency trade-offs than conventional sparse training. Finally, we evaluate whether such identified SuperTickets drawn from one task can transfer well to other tasks, validating their potential of simultaneously handling multiple tasks. Extensive experiments and ablation studies on three tasks and four benchmark datasets validate that our proposed SuperTickets achieve boosted accuracy and efficiency trade-offs than both typical NAS and pruning pipelines, regardless of having retraining or not. Codes and pretrained models are available at https://github.com/RICE-EIC/SuperTickets .