Reviewerly/exHarmony huggingface dataset:
A Chinese L2 Learners' Dynamic Vocabulary Growth Network Model Based on Graph Deep Learning

This paper regards vocabulary networks mastered by Chinese second language(L2) learners at different levels as sub graphs of a Chinese Word Co-occurrence Network, embeds these subgraphs with the help of graph deep learning techniques such as TSPMiner model and Order Embedding algorithm, and builds a dynamic vocabulary growth network model for the learners. This model can predict nodes and links between nodes, simulate the growth process of a learner vocabulary, so as to offer guidance to learners. With this model, a smooth, efficient, and dynamic adaptive vocabulary learning process becomes possible on learning platforms. Through a questionnaire and data analysis on it, the model is verified in that participating Chinese teachers have great consistency with model recommended word learning sequences.
Improving Energy Efficiency of Convolutional Neural Networks on Multi-core Architectures through Run-time Reconfiguration

Convolutional neural networks (CNNs) are built with convolution layers which account for most of their computation time. The differences in the convolution kernel types (2D, point-wise, depth-wise), and input sizes lead to significant differences in their computation and memory demands. In this work, we exploit run-time reconfiguration to adapt to the differences in the characteristics of different convolution kernels on a low-power reconfigurable architecture, Transmuter. The architecture consists of light-weight cores interconnected by caches and crossbars that support run-time reconfiguration between different cache modes - shared or private, different dataflow modes - systolic or parallel, and different computation mapping schemes. To achieve run-time reconfiguration, we propose a decision-tree-based engine that selects the optimal Transmuter configuration at a low cost. The proposed method is evaluated on commonly-used CNN models such as ResNetl8, VGGII, AlexNet and MobileNetV3. Simulation results show that run-time reconfiguration helps improve the energy efficiency of Transmuter in the range of 3.1$\times-13.7\times$ across all networks.
Peripeteia and Canonicity

Starting from Aristotle’s use of the term, the concept of peripeteia is examined first from the point of view of poetics in the Greek world and the way stories are constructed then as a notion that connects to that of deviation from the norm. The inevitable ambiguity between canonicity and violation or between exception and rule is emphasized as the one concept refers to the other. This ambiguity is also linked to the fact that a deviation is likely to become a norm and vice versa a norm can become a violation. Violation and norm underlie many cognitive theories, as well as disciplines such as statistics and law. Another reason why the difference between ordinary and violation is ambiguous is that ordinary, beyond appearances, is not such a simple concept to explore because it presupposes assumptions or subtexts that are taken for granted and shared but which, if they are not, can give rise to communicative misunderstandings. Rule and exception accompany the whole of people’s life, during which they have to deal with the ambiguity of the boundaries among different categories, memories, and rules.
Transformer-based Scene Graph Generation Network With Relational Attention Module

Scene graph generation is a fundamental task for visual understanding, and the generated scene graphs are leveraged in various downstream tasks. Most existing methods rely only on the features cropped by bounding boxes to predict the relationship between subject and object. This indicates that the region outside the subject and object is ignored. Therefore, these methods lack contextually important information existing outside of the subject and object regions. Furthermore, recently, it has been pointed out that unannotated instances in the training dataset can lead to false suppression of the valid predictions of the model. To this end, we propose a novel transformer-based network and a training scheme for our model. We introduce the relational attention module to overcome the cropped feature problem. This module can adaptively extract the contextually important regions from the entire image via the attention mechanism for each entity pair whose relationship is predicted. Moreover, to train our model, we design a training strategy with instance-level pseudotargets. This design can solve the incomplete annotation problem by appending the instances generated by the trained model as a pseudo-target. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our key designs, and our model achieves state-of-the-art or competitive performance in all tasks: PredCls, SGCls, and SGDet on the Visual Genome dataset.
New Block Cipher Encryption Based On OTP Algorithm

The current world revolves around encryption. Every time a mobile phone call is made, a credit card is used in a store or on the Internet, or to withdraw cash from an ATM, encryption ensures the transaction’s secrecy and security. In this paper, we propose a high secure practical OTP Algorithm using CTR encryption. In this paper, we provide a solution that is unbreakable by quantum computing. Quantum computing is the highest threat facing encryption nowadays. SHA-512 and PRNG are applied in this paper within the proposed mathematical model. and the model was deeply analyzed. Also, security and practicality were proven.
Trajectory reconstruction system of moving target applied in volleyball match

For the tactical analysis in sports video, the trajectory of ball and players is very valuable data. Therefore, this paper puts forward a system of reconstructing the trajectory of ball and players from volleyball videos. The main tasks include player detection and tracking, trajectory repair and Volleyball scene 2D mapping. According to the fixed competition area, the Yolov5 algorithm is used to train the detector, and vol-DeepSORT is combined to realize the real-time detection and tracking of the players, which can reduce the problems of missed detection and false detection caused by long-term occlusion and lens running. Finally, the camera calibration is used to reconstruct and display the trajectory information of the ball and players, which is helpful for professionals to know effectively and comprehensively the tactical application in volleyball matches.
A DSPL and Reinforcement Learning Approach for Context-Aware IoT Systems Development

The internet of things is a paradigm of interconnected devices able to communicate and exchange information to achieve users' requirements. In spite of their expansion in the last years, IoT systems still face challenges that hinder gaining major advantages from them. One of these challenges is to automatically adapt the system to the user's context and preferences. As a proposition to deal with this problem, this paper presents a methodology to design and develop IoT systems that adapt their behavior to their context, which can be a user or environmental context. This methodology is based on dynamic software product line engineering and uses Markov process to design the adaptation plan of the system and a reinforcement learning algorithm to implement it.
Detect Deeply Only Once: Arbitrary-Oriented Object Detection in Remote Sensing Images Based on Improved YOLOv5

To solve the problem that the existing arbitrary-oriented object detection cannot satisfy both high accuracy and high efficiency, a model called Detect Deeply Only Once is proposed. The model is based on the YOLOv5s_CSL network and is improved in three ways: (1) Three attention mechanisms are separately added to the appropriate positions of the model. All three attention mechanisms pay more attention to the features of interest and improve the detection accuracy. (2) The repeated weighted bi-directional feature pyramid network and path aggregation network are combined, which strengthens feature fusion ability among multiple layers. (3) Loss rank mining is employed after the final feature map, which filters some elements representing easy examples in the final feature map and forces the detectors to concentrate on hard examples during training. Meanwhile, the role of each module is verified on the HRSC2016 dataset, and the proposed method shows good superiority in terms of higher detection accuracy than the benchmark model and the state-of-the-art models on both HRSC2016 dataset and UCAS_AOD dataset.
Packet Chunking for File Detection

Network-based intrusion detection and data leakage prevention systems inspect packets to detect if critical files such as malware or confidential documents are transferred. However, this kind of detection requires heavy computing resources in reassembling packets and only well-known protocols can be interpreted. Besides, finding similar files from a storage requires pairwise comparisons. In this paper, we present a new network-based file identification scheme that inspects packets independently without reassembly and finds similar files through inverted indexing instead of pairwise comparison. We use a content-based chunking algorithm to consistently divide both files and packets into multiple byte sequences, called chunks. If a packet is a part of a file, they would have common chunks. The challenging problem is that packet chunking and inverted-index search should be fast and scalable enough for packet processing. The file identification should be accurate although many chunks are noises. In this paper, we use a small Bloom filter and a two-level threshold strategy to solve the problems. To the best of our knowledge, this is the first scheme that identifies a specific critical file from a packet over unknown protocols. Experimental results show that the proposed scheme can successfully identify a critical file from a packet without packet reassembly.
Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation

Graph neural networks (GNNs) have achieved outstanding performance in semi-supervised learning tasks with partially labeled graph structured data. However, labeling graph data for training is a challenging task, and inaccurate labels may mislead the training process to erroneous GNN models for node classification. In this paper, we consider label poisoning attacks on training data, where the labels of input data are modified by an adversary before training, to understand to what extent the state-of-the-art GNN models are resistant/vulnerable to such attacks. Specifically, we propose a label poisoning attack framework for graph convolutional networks (GCNs), inspired by the equivalence between label propagation and decoupled GCNs that separate message passing from neural networks. Instead of attacking the entire GCN models, we propose to attack solely label propagation for message passing. It turns out that a gradient-based attack on label propagation is effective and efficient towards the misleading of GCN training. More remarkably, such label attack can be topology-agnostic in the sense that the labels to be attacked can be efficiently chosen without knowing graph structures. Extensive experimental results demonstrate the effectiveness of the proposed method against state-of-the-art GCN-like models.
UC Analysis of the Randomized McEliece Cryptosystem

In this paper, we will examine the UC (Universal Composability) security of an improved version of the McEliece cryptosystem, namely the Randomized McEliece cryptosystem. We will prove that even this improved variant does not realize $$\mathcal {F}_\texttt{PKE}$$ , the public key encryption functionality securely.
A Fairness-Aware Cooperation Strategy for Multi-Agent Systems Driven by Deep Reinforcement Learning

The research on multi-agent cooperation strategies has been attracting widespread concerns in recent years. However, the current deep reinforcement learning algorithms mainly focus on improving cooperation efficiency while ignore fairness. Taking into account both collaboration efficiency and fairness is a complex multi-objective optimization problem. To address this concern, we design a Fair-Efficiency Multi-Agent Deep Deterministic Policy Gradient (FE-MADDPG) algorithm. First, we design a fair and efficient reward function which sets the resource occupancy rate as the ratio of each agent's average reward to the total reward to ensure the fairness of each agent. Then, we improve the MADDPG algorithm by utilizing the reward function and make comparisons of the efficiency of agents. Finally, we employ the Gini coefficient and the time consumed for completing the task as evaluation indicators to verify the fairness and efficiency. Simulation results show that the FE-MADDPG algorithm significantly improves the efficiency of the system under the premise of ensuring fairness for each agent.
Atomically True Ontology Modelling: Residential Buildings

Creation of realistic residential buildings has been the topic of research in procedural content generation (PCG) for several years. Most PCG methods rely on analysing domain knowledge in the applicable setting and encoding that knowledge as configuration for the PCG methods used. While this technique does solve the immediate problem, it is often not portable, and extensions of a specific generation technique often requires reconfiguring when new or more detailed domain knowledge is attained. This paper presents a holistic ontological driven PCG technique that allows for varied PCG methods to be used and interchanged while having a stable platform for domain knowledge. The technique presented in the paper is an implementation of a PCG framework: atomically true ontology modelling (ATOM). Using ATOM for creating residential buildings allows for the definition of general concepts such as rooms and room types that can be then used to drive PCG methods to produce residential buildings in distinctive styles and environments.
Semi-Supervised Fine-Grained Classification with Web Data via Noisy Sample Selection

For fine-grained classification, it is extremely difficult and costly to acquire the annotated data. Hence, some studies propose to use web data for fine-grained classification. However, the web data contains tremendous noisy labels, which can affect the classification results. Although many previous studies propose to discard noisy data via sample selection, they also discard some valid data. The valid data denotes hard or mislabeled samples that can enhance the robustness of the model. To solve the above problems, we propose a novel method to discard irrelevant noisy data from web data while keeping valid data for fine-grained classification. Specifically, we divide the web data into clean and noisy samples and then distinguish the noisy samples into open-set and close-set noises. Finally, the model is constructed in a semi-supervised manner, where the clean samples are used as the labeled set, and the close-set noises are used as the unlabeled set. Extensive experiments verify that our method can improve the classification performance by an average of 1.89% on three fine-grained benchmark datasets compared with the current methods. The experimental results prove the effectiveness of the combination of sample selection and semi-supervised training strategy.
The Advance of the Combination Method of Machine Learning and Deep Learning

The deep learning technology represented by the neural network has made remarkable achievements in recent years. However, deep learning technology has reached the bottleneck period due to interpretability, data and computing demand, and other reasons. One solution is to combine some classic machine learning methods with deep learning techniques to alleviate the existing problems. This paper summarizes the representative work of combining the machine learning method and deep learning method in recent years. It analyzes the advantages and disadvantages of these kinds of methods. In addition, this paper analyzes and discusses the challenges and development prospects of deep learning technology. We believe that the review and analysis of the above work and the discussion of the current predicament will provide valuable reference information for the development of artificial intelligence.
Unsupervised Cross-domain Person Re-Identification based on Asymmetrical Pyramid Non-local Block

The purpose of unsupervised cross-domain (UCD) person re-identification (re-ID) is to adapt the well pre-trained model on the labeled source domain to the unlabeled target domain, which tackles a more realistic problem. However, the network in the existing model cannot fully extract the features of pedestrians, so the results after clustering are not satisfactory. To address this problem, a feature extraction network model with a self-attention mechanism is proposed in this paper in order to improve the feature expression ability. We try to design and optimize the attention mechanism-based feature extraction network and similarity loss function for unsupervised person re-ID to improve the recognition accuracy. On the basis of the baseline network (such as ResNet-50), the self-attention mechanism-asymmetrical pyramid non-local block (APNB) is added to help the network learn richer global feature representation. Besides, the similarity loss function using the Euclidean distance is designed, which shows better performance than the cosine distance. Experimental results show that the proposed method has competitive performance on two public datasets Markket-1501 and DukeMTMC-Re-ID.
A Sound Strategy to Compile General Recursion into Finite Depth Pattern Matching

Programming languages are popular and diverse, and the convenience of programmatically changing the behavior of complex systems is attractive even for the ones with stringent security requirements, which often impose restrictions on the acceptable programs. A very common restriction is that the program must terminate, which is very hard to check because the Halting Problem is undecidable. In this work, we proposed a technique to unroll recursive programs in functional languages to create terminating versions of them. We prove that our strategy itself is guaranteed to terminate. We also formalize term generation and run property-based tests to build confidence that the semantics is preserved through the transformation. Our strategy can be used to compile general purpose functional languages to restrictive targets such as the eBPF and smart contracts for blockchain networks.
Context-aware Deformable Alignment for Video Object Segmentation

Matching-based Semi-supervised video object segmentation (VOS) either resorts to non-local matching to retrieve and aggregate the spatiotemporal features of past frames or relies on template matching to learn similarity representation. Although achieving remarkable progress, they still suffer from considerable computation overhead and failure when confronting with large appearance changes, respectively. In this paper, we are motivated to address the above issues. Firstly, we propose a Context-aware Deformable Alignment (CDA) mechanism to align the spatio-temporal features of past frames more efficiently. To reduce computation complexity dramatically and retain the ability of modeling long-range spatio-temporal dependencies, the CDA mechanism that learns where to match in a deformable fashion belongs to local context-aware matching instead of nonlocal pixel-wise matching. Furthermore, we present a Dynamic Kernel Matching (DKM) technique to tackle the mismatches due to appearance and scale variations. DKM dynamically adapts the template feature to object appearance changes rather than fixing it, which improves the robustness for long-term VOS. Our framework dubbed CDANet is evaluated on popular benchmark sets, which achieves competitive performance compared with SOTA methods.
Double Auction used Artificial Neural Network in Cloud Computing

Double auction (DA) algorithm is widely used for trading systems in cloud computing. Distinct buyers request different attributes for virtual machines. On the other hand, different sellers offer several types of virtual machines according to their correspondence bids. In DA, getting multiple equilibrium prices from distinct cloud providers is a difficult task, and one of the major problems is bidding prices for virtual machines, so we cannot make decisions with inconsistent data. To solve this problem, we need to find the best machine learning algorithm that anticipates the bid cost for virtual machines. Analyzing the performance of DA algorithm with machine learning algorithms is to predict the bidding price for both buyers and sellers. Therefore, we have implemented several machine learning algorithms and observed their performance on the bases of accuracy, such as linear regression (83%), decision tree regressor (77%), random forest (82%), gradient boosting (81%), and support vector regressor (90%). In the end, we observed that the Artificial Neural Network (ANN) provided an astonishing result. ANN has provided 97% accuracy in predicting bidding prices in DA compared to all other learning algorithms. It reduced the wastage of resources (VMs attributes) and soared both users' profits (buyers &amp; sellers). Different types of models were analyzed on the bases of individual parameters such as accuracy. In the end, we found that ANN is effective and valuable for bidding prices for both users.
A flat reachability-based measure for CakeML’s cost semantics

The CakeML project has recently developed a verified cost semantics that allows reasoning about the space safety of CakeML programs. With this space cost semantics, compiled machine code can be proven to have tight memory bounds ensuring no out-of-memory errors occur during execution. This paper proposes a new cost semantics which is designed to make proofs about space safety significantly simpler than they were with the original version. The work described here has been developed in the HOL4 theorem prover.
Alignment Format for Semantic Translation

Abundance of vastly heterogeneous, high-volume/high-velocity data producers/consumers, predominantly caused by proliferation of IoT-based solutions, results in an urgent need for efficient semantic interoperability solutions. Hence, the need to solve the problems of domain understanding, domain formal representation, and expression of mappings between different data models arise. In this contribution, we present an alignment format called IPSM-AF, for persisting correspondences between ontologies that can be used for semantic translation. Specifically, alignments represented using the IPSM-AF can be efficiently parsed and consumed by the IPSM tool, which performs the actual translation (possibly on streaming data). The proposed format is compliant with the Alignment API format, level 2.
Research Advanced in Deep Learning Based Image Recognition

Image recognition has already been a popular discussion widely studied in computer vision community, which aims to predict the category of each image to distinguish different images. The traditional image recognition methods struggle to process images of large order of magnitude, taking less time and yielding unsatisfactory results. In recent years, benifiting from the powerful feature expression ability of deep convolutional neural networks, both the recognization accuracy and speed of deep learning based mage recognition methods have made a breakthrough, which replacing the traditional methods and becoming the mainstream method of image recognition. In this paper, based on detailed literature research and analysis, we conduct a comprehensive survey on existing methods of image recognition based on deep learning. Specifically, we first introduce the preferential neural networks including self-encoder, convolutional neural network, etc. We then introduce the application of image recognition in various fields and give a series of experiments to analyze the achievement of different detection algorithms on some common datasets. Finally, we try to speculate on the possible future directions of image recognition to make it practical.