Reviewerly/exHarmony huggingface dataset:
Lightweight target detection algorithm based on improved YOLOv4

Traditional YOLOv4 object detection network is difficult to be applied to mobile embedded devices because it has some deficiencies such as complex structure and too many parameters. In this paper, the authors propose a lightweight object detection algorithm. Firstly, Mobienetv3 is used to replace the original feature extraction network, and the Mish function is used to be the activation function, which reduces the number of model parameters. Secondly, the dilated convolution is used to replace the maximum pooling operation in the original spatial pyramid pooling (SPP) structure. Then, a custom Dcn-Dw structure is used to replace the convolution operation in the original PANet, which improves the accuracy of the model for irregular object detection and reduces the model size. Finally, a CBAM lightweight attention mechanism module is introduced in front of the YOLO Head, which further improves the model accuracy. An experiment on the VOC2007 dataset is carried out, and the results show that the mean average precision (mAP) is 80.3% and frames per second (FPS) is 15. At the expense of 3% accuracy, the detection speed is increased to two to three times, and the model size is reduced to one-fifth of the original model. The lightweight object detection algorithm can suit for real-time detection tasks on resource-constrained embedded devices.
Cross-Lingual Plagiarism Detection Method

In this paper, we describe a method for cross-lingual plagiarism detection for a distant language pair (Russian-English). All documents in a reference collection are split into fragments of fixed size. These fragments are indexed in a special inverted index, which maps words to a bit array. Each bit in the bit array shows whether a $$i_{th}$$ sentence contains this word. This index is used for the retrieval of candidate fragments. We employ bit arrays stored in the index for assessing similarity of query and candidate sentences by lexis. Before doing retrieval, top keywords of a query document are mapped from one language to other with the help of cross-lingual word embeddings. We also train a language-agnostic sentence encoder that helps in comparing sentence pairs that have few or no lexis in common. The combined similarity score of sentence pairs is used by a text alignment algorithm, which tries to find blocks of contiguous and similar sentence pairs. We introduce a dataset for evaluation of this task - automatically translated Paraplag (monolingual dataset for plagiarism detection). The proposed method shows good performance on our dataset in terms of F1. We also evaluate the method on another publicly available dataset, on which our method outperforms previously reported results.
Diving Deep into Context-Aware Neural Machine Translation

Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation, or having meta-information. Although there exist various architectures and analyses, the effectiveness of different context-aware NMT models is not well explored yet. This paper analyzes the performance of document-level NMT models on four diverse domains with a varied amount of parallel document-level bilingual data. We conduct a comprehensive set of experiments to investigate the impact of document-level NMT. We find that there is no single best approach to document-level NMT, but rather that different architectures come out on top on different tasks. Looking at task-specific problems, such as pronoun resolution or headline translation, we find improvements in the context-aware systems, even in cases where the corpus-level metrics like BLEU show no significant improvement. We also show that document-level back-translation significantly helps to compensate for the lack of document-level bi-texts.
Toward a practical legal rule revision in legal debugging

Computational law researchers have long been interested in developing machine legal reasoning systems and machine-readable rule-bases to simulate legal reasoning given by judges. Basically, legal rule-bases rely on literal interpretations of statutes. In practice, however, literal interpretation of statute can have counterintuitive consequences. To deal with such consequences, English courts (the courts of England and Wales) have developed rules of judicial interpretation for reinterpreting statutes i.e. choosing or modifying the meaning of the statute’s wording (golden rule) or determining the statute’s purpose (mischief rule), and these rules have not been investigated in computational law. Previous research proposed the concept of legal debugging to formalize detection and resolution of counterintuitive consequences in legal rule-bases. Legal debugging consists of two steps. The first step is culprit detection, which involves interacting with a judge in order to identify a culprit, which is intuitively a part of statute that can be determined as a root cause of counterintuitive consequences. The second step is culprit resolution, which involves revising the rule-base to resolve a detected culprit. In this paper, we match two inductive logic programming approaches to two rules of judicial interpretation. The first approach is a bottom-up approach, which involves identifying an exceptional situation of the case and generalizing the legal rule revision. We match the bottom-up approach to the golden rule by using knowledge bases in the form of legal ontologies. The second approach is a top-down approach, which involves determining the purpose of the statute and refining the legal rule revision so that the revision fits within the context of the statute. We match the top-down approach to the mischief rule by using knowledge bases in the form of metarules. By using these matches, legal debugging can perform more practical legal revisions during the culprit resolution step.
Time-Division Multiplexing Ising Computer Using Single Stochastic Magnetic Tunneling Junction

A magnetic tunneling junction (MTJ) with very low energy barrier, which shows controllable stochastic property, is recently proposed to constitute the probability bit (P-Bit). With the P-Bit, Ising computation, which is a general and powerful computing platform for both conventional and non-conventional problems, is implemented. However, such hardware emulation suffers from the severe intrinsic variations of a stochastic MTJ device. The nonuniform probability switching curves of different stochastic MTJs hinder straightforward expansion of Ising computer to a large P-Bits array for solving more complicated problems. In this work, we propose a novel Ising computer using a single stochastic MTJ device. With the utilization of the time-division multiplexing (TDM) technology, a “Compute–Read–Switch” scheme is proposed, so that the single stochastic MTJ can act as multiple P-Bits during Ising computation. A design guide for the current magnitude and time duration of the “Compute–Read–Switch” scheme is investigated and provided. Furthermore, NOT and XOR logic gates are implemented with our proposal. An accuracy rate as high as 69% for the integer factorization is also achieved, which is comparable with conventional Ising computer. Meanwhile, the TDM Ising computer avoids the calibration process, which is mandatory in the conventional Ising computer.
Intelligent and compliant dynamic software license consolidation in cloud environment

Based on the virtualization technology and pushed by the softwarization paradigm and the actual demand for services and resources, commercial cloud data centers know an unprecedented expansion. The systematic presence of software and the services generated enabled the development of the already dense application expenses. This causes, not only a cost explosion, especially when proprietary solutions protected by licenses are in hand, but also, represents a critical need in terms of software asset and resource management at the SaaS level. In addition to these costs, inefficient resource utilization, and the resulting energy represent an important part of the operational expenditure of data centers and are still a hot topic despite the consolidation initiatives put in place. The main objective of the consolidation service is to maximize resource exploitation while minimizing energy consumption and costs, among others. Even so, we have noticed that the reported literature doesn't treat license management in the cloud environment as a whole, especially, from the resource management perspective and the overwhelming majority of the consolidation work focuses on resource optimization at the IaaS level. Therefore, we propose a reinforcement learning-based scheme that allows efficient use of resources and optimizes costs, energy consumption, and resource wastage, while remaining compliant. The experimental results show that our intelligent consolidator outperforms the baseline approaches according to the evaluation metrics used regardless of the resource heterogeneity and the data center dimensionality.
Continuous variable quantum discord in a cavity electrooptical down-conversion quantum system

In this paper, we explore continuous variable quantum discord for the stationary microwave–optical output fields in an electrooptical system. We drive the covariance matrix of the system using the quantum Langevin equations for the intera-cavity microwave and optical modes. Employing the input–output theory, we obtained the output microwave and optical resonator variables in the Fourier frequency domain. The properties of the Gaussian quantum discord with respect to electrooptical cooperativity, the normalized cavity wave guides for microwave and optical resonators, and the thermal mean photon number for microwave and optical modes have been analyzed. Henceforth, we obtained that the Gaussian quantum discord can be enhanced by increasing the electrooptical cooperativity and microwave and optical wave guides. The thermal mean photon numbers of the microwave and optical modes have reduction effects on the Gaussian quantum discord.
Detecting fraudulent transactions in banking cards using scale‐free graphs

Abstract This study aims to present an effective algorithm for identifying fraudulent transactions of banking cards. The proposed method uses missing value replacements and scale‐free graphs and the ensemble method with a large number of graphs that utilized voting between all graphs to predict whether a transaction is authorized or suspicious. The purpose of this method is to develop a graph‐based system for detecting fraudulent activities in the banking industry. This research improves the evaluation criteria and eliminates the weakness of individual methods and other group methods. This is achieved by combining group capability and group decision‐making and free scaling of scale‐free graphs. This was confirmed through conducting different experiments on two standard datasets and comparing with different studies and individual methods. The unique features of the proposed model included estimating the generalization error in the implementation as well as no need for assessment methods or test stage, along with estimating the level of importance of each variable in the problem during the algorithm runtime. To evaluate this algorithm, various parameters that are used in many data mining methods have been employed. The proposed algorithm is assessed against the decision tree algorithm, support vector machine, neural network, and ensemble neural network. Based on the obtained results, it was observed that in both datasets, the amount of evaluation criteria including accuracy, sensitivity, and f‐criteria calculated in the proposed method has increased by approximately 20% compared to the above‐mentioned our methods.
Research on the Value and Path of Cultivating College Students' Digital Literacy in the Digital Age

With the development and application of digital technologies such as artificial intelligence, big data and 5G, digital literacy has become the core element and important requirement of college students' literacy. In the current digital era, strengthening the cultivation of college students' digital literacy has its intrinsic value: it is the inevitable requirement for the survival and development of the digital society, the prerequisite for improving the digital literacy of the whole people, and the inevitable choice for realizing the strategic construction goal of digital China. In order to promote the cultivation of college students' digital literacy, this paper constructs the cultivation path of college students' digital literacy from the perspective of activity theory, which is composed of subject dimension, community and labor-division dimension, tool dimension and rule dimension, so as to meet the requirements of college students' digital literacy in the digital era.
Modulation Format Recognition based on Transfer Learning for Visible Light Communication Systems

We demonstrate a novel modulation format recognition method using transfer learning for OFDM-VLC systems. The proposed AlexNet/GoogLeNet-TL-based scheme significantly improves recognition accuracy over a large SNR range in practical scenarios with small training datasets available.
FenceMask: A Data Augmentation Approach for Pre-extracted Image Features

We propose a novel data augmentation method named 'FenceMask' that exhibits outstanding performance in various computer vision tasks. It is based on the 'simulation of object occlusion' strategy, which aim to achieve the balance between object occlusion and information retention of the input data. By enhancing the sparsity and regularity of the occlusion block, our augmentation method overcome the difficulty of small object augmentation and notably improve performance over baselines. Sufficient experiments prove the performance of our method is better than other simulate object occlusion approaches. We tested it on CIFAR10, CIFAR100 and ImageNet datasets for Coarse-grained classification, COCO2017 and VisDrone datasets for detection, Oxford Flowers, Cornel Leaf and Stanford Dogs datasets for Fine-Grained Visual Categorization. Our method achieved significant performance improvement on Fine-Grained Visual Categorization task and VisDrone dataset.
Enumerating teams in first-order team logics

We start the study of the enumeration complexity of different satisfiability problems in first-order team logics. Since many of our problems go beyond DelP, we use a framework for hard enumeration analogous to the polynomial hierarchy, which was recently introduced by Creignou et al. (Discret. Appl. Math. 2019). We show that the problem to enumerate all satisfying teams of a fixed formula in a given first-order structure is DelNP-complete for certain formulas of dependence logic and independence logic. For inclusion logic formulas, this problem is even in DelP. Furthermore, we study the variants of this problem where only maximal or minimal solutions, with respect to cardinality or inclusion, are considered. For the most part these share the same complexity as the original problem. One exception is the cardinality minimum-variant for inclusion logic, which is DelNP-complete, the other is the inclusion maximal-variant for dependence and independence logic, which is in Del+NP and DelNP-hard.
Speech Fusion to Face: Bridging the Gap Between Human's Vocal Characteristics and Facial Imaging

While deep learning technologies are now capable of generating realistic images confusing humans, the research efforts are turning to the synthesis of images for more concrete and application-specific purposes. Facial image generation based on vocal characteristics from speech is one of such important yet challenging tasks. It is the key enabler to influential use cases of image generation, especially for business in public security and entertainment. Existing solutions to the problem of speech2face renders limited image quality and fails to preserve facial similarity due to the lack of quality dataset for training and appropriate integration of vocal features. In this paper, we investigate these key technical challenges and propose Speech Fusion to Face, or SF2F in short, attempting to address the issue of facial image quality and the poor connection between vocal feature domain and modern image generation models. By adopting new strategies on data model and training, we demonstrate dramatic performance boost over state-of-the-art solution, by doubling the recall of individual identity, and lifting the quality score from 15 to 19 based on the mutual information score with VGGFace classifier.
A Mathematical Perspective on Post-Quantum Cryptography

In 2016, the National Institute of Standards and Technology (NIST) announced an open competition with the goal of finding and standardizing suitable algorithms for quantum-resistant cryptography. This study presents a detailed, mathematically oriented overview of the round-three finalists of NIST’s post-quantum cryptography standardization consisting of the lattice-based key encapsulation mechanisms (KEMs) CRYSTALS-Kyber, NTRU and SABER; the code-based KEM Classic McEliece; the lattice-based signature schemes CRYSTALS-Dilithium and FALCON; and the multivariate-based signature scheme Rainbow. The above-cited algorithm descriptions are precise technical specifications intended for cryptographic experts. Nevertheless, the documents are not well-suited for a general interested mathematical audience. Therefore, the main focus is put on the algorithms’ corresponding algebraic foundations, in particular LWE problems, NTRU lattices, linear codes and multivariate equation systems with the aim of fostering a broader understanding of the mathematical concepts behind post-quantum cryptography.
Open Dataset Recorded by Single Cameras for Multi-Player Tracking in Soccer Scenarios

Multi-player action recognition for automatic analysis in sports is the subject of increasing attention. Trajectory-tracking technology is key for accurate recognition, but little research has focused on this aspect, especially for non-professional matches. Here, we study multi-player tracking in the most popular and complex sport among non-professionals—soccer. In this non-professional soccer player tracking (NPSPT) challenge, single-view-based motion recording systems for continuous data collection were installed in several soccer fields, and a new benchmark dataset was collected. The dataset consists of 17 2-min long super-high-resolution videos with diverse game types consistently labeled across time, covering almost all possible situations for multi-player detection and tracking in real games. A comprehensive evaluation was conducted on the state-of-the-art multi-object-Tracking (MOT) systems, revealing insights into player tracking in real games. Our challenge introduces a new dimension for researchers in the player recognition field and will be beneficial to further studies.
A New Approach to Determine the Minimal Polynomials of Binary Modified de Bruijn Sequences

A binary modified de Bruijn sequence is an infinite and periodic binary sequence derived by removing a zero from the longest run of zeros in a binary de Bruijn sequence. The minimal polynomial of the modified sequence is its unique least-degree characteristic polynomial. Leveraging a recent characterization, we devise a novel general approach to determine the minimal polynomial. We translate the characterization into a problem of identifying a Hamiltonian cycle in a specially constructed graph. The graph is isomorphic to the modified de Bruijn–Good graph. Along the way, we demonstrate the usefulness of some computational tools from the cycle joining method in the modified setup.
Research on solid waste plastic bottle cognitive based on YOLOv5s and deep stochastic configuration network

To address the problems that the efficiency and accuracy of solid waste plastic bottle detection in complex environments are difficult to meet the requirements, this paper explores a solid waste plastic bottle cognition method based on YOLOv5s and deep stochastic configuration network. First, a normalized high-quality solid waste image sample collection is constructed; second, a solid waste image depth feature space is constructed based on YOLOv5s to characterize the solid waste plastic bottle feature information at multiple levels of differentiation; finally, a solid waste plastic bottle classifier is constructed based on a deep stochastic network to obtain a fast and high-accuracy classification model. In order to verify the effectiveness of this paper, the training and testing sample space is constructed by taking solid waste image samples with Hikvision MV-CE050-30GM camera, and this paper is compared with other model methods. The experimental results show that the method in this paper has greater advantages in the comprehensive evaluation of model cognitive accuracy and computational speed, and can be effectively applied to the intelligent sorting of solid waste plastic bottles to improve the work efficiency.
Improvement of Kernel Correlation Filtering Algorithm Based on Kalman Filter

Kernel correlation filtering algorithm is based on the concept of correlation filtering. By introducing methods such as kernel functions and cyclic matrices, the target tracking speed is improved to a new level while ensuring high tracking accuracy. However, its tracking effect is not good when the target changes at multiple scales and the target is blocked. In view of the above problems, this paper proposes a multi-scale calculation method based on depth information optimization to solve the problem of multi-scale changes in the target. Based on the introduction of occlusion detection, a kernel-correlation filtering algorithm based on Kalman filtering is proposed to solve the problems of occlusion and jitter problem. The improved algorithm is tested on the TOB-50 data set and compared with the original algorithm. The results show that the improved algorithm proposed in this paper has better performance than the original algorithm when the target scale changes, occlusion, and screen jitter.
Obstacle-avoidance path planning and tracking control of an autonomous vehicle

This paper proposes an artificial potential field (APF) method to plan the obstacle-avoidance path for an autonomous vehicle, and transforms the discrete path model into a continuous one for path-tracking control design. To describe the lateral dynamics and path-tracking characteristics of the vehicle, a two-degree-of-freedom (2-DOF) dynamic model and a kinematic model are established. According to the reference path information and vehicle dynamic model, a sliding mode control (SMC) strategy is presented to realize the vehicle’s path-tracking behaviour, and the stability of control system is testified in Lyapunov sense. Finally, MATLAB/Simulink-Carsim co-simulation is applied to test the effectiveness of the proposed method under various road conditions.