Reviewerly/exHarmony huggingface dataset:
A Grouping Design for Cascaded Coded Distributed Computing

In order to reduce the communication load in general distributed computing frameworks such as MapReduce Li et al. introduced coded distributed computing (CDC). The authors in [1] proposed a scheme for cascaded coded distributed computing on general networks. Although the numbers of input files and output functions in [1] are much less than those in [2], large numbers of input files and output functions are still required. In this paper, we present a new CDC scheme which 1) designs the assignments of input file and output function; 2) requires less number of input files and output functions compared with those in the scheme derived by [1]. Meanwhile, the ratio of communication load to that of the scheme in [1] is less than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{the}\frac{4}{3}$</tex> .
T-PPA: A Privacy-Preserving Decentralized Payment System with Efficient Auditability Based on TEE

Cryptocurrencies such as Bitcoin and Ethereum achieve decentralized payment by maintaining a globally distributed and append-only ledger. Recently, several researchers have sought to achieve privacy-preserving auditing, which is a crucial function for scenarios that require regulatory compliance, for decentralized payment systems. However, those proposed schemes usually cost much time for the cooperation between the auditor and the user due to leveraging complex cryptographic tools such as zero-knowledge proof. To tackle the problem, we present T-PPA, a privacy-preserving decentralized payment system, which provides customizable and efficient auditability by leveraging trusted execution environments (TEEs). T-PPA demands the auditor construct audit programs based on request and execute them in the TEE to protect the privacy of transactions. Then, identity-based encryption (IBE) is employed to construct the separation of power between the agency nodes and the auditor and to protect the privacy of transactions out of TEE. The experimental results show that T-PPA can achieve privacy-preserving audits with acceptable overhead.
Development of a Smart Wearable Device for Real-Time Activity Detection

This paper presents a wearable real-time activity detection device which is based on a 9-axis IMU and a microcontroller. By applying SVM algorithm and with high-speed sampled data, the system is expected to give an accurate human activity detection result, which can be used in the healthcare of the elder people or activity monitoring. An idea of introducing a kind of output filter into the classification algorithm has been proved to improve the performance of detection accuracy, by an experiment on hundreds of action samples collected from 8 different people.
Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach.
Backward Edge Pointer Protection Technology Based on Dynamic Instrumentation

Buffer overflow is an important security issue. Many countermeasures have been proposed, e.g., DEP, shadow stack. However, existing DEP is facing the problem of being bypassed by attackers through information leakage attacks. To address the shortcomings of current backward edge pointer integrity protection in preventing buffer overflow attacks, this paper proposes a shadow stack based backward edge pointer integrity protection scheme through dynamic instrumentation, and implements a prototype system based on the DynamoRIO framework. Shadow stack mechanism can achieve effective protection for return address integrity by maintaining a one-to-one mapping relationship between the shadow stack and the program stack. Meanwhile, test cases were designed to evaluate the effectiveness, runtime performance and compatibility of the plugin. Experiment results show that the security plugin is compatible with most programs and can run with an average overhead of about 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> to 15%. This is acceptable when comparing to stack shield with 22% overhead.
A Neural Network Binary Quantization Method Based on W-Regularization and Variable Cosine Momentum

To solve the problem of insufficient extraction of weight information in binary quantization, this paper proposes a new training module based on W-regularization and variable cosine momentum. W-regularization is achieved by adjusting the network weights so that the weight values are optimised to ±1 and the parameters at different positions are optimised according to different functions. In addition, variable cosine momentum is designed so that parameters farther away from ±1 approach zero at high speed, which can significantly increase the speed of convergence and further improve quantization accuracy. Specifically, it outperforms the highest accuracy of bnn-free by 0.83% and 2.15% on the CIFAR-10, CIFAR-100 datasets, it also improved on both SVHN and TinyImage.
Pyramid Attention CycleGAN for Non-Parallel Voice Conversion

Non-parallel voice conversion (VC) is a voice mapping technology that uses non-parallel corpus to convert source speeches into target speeches while maintaining semantic information unchanged. Cycle-consistent adversarial network-based VC with Filling in Frames (MaskCycleGAN-VC) is proposed and generally accepted as a current benchmark method. While it solves the problem of time-frequency structures consistency, the performance of voice conversion is not satisfactory enough. There is still a large gap between target and converted voice in terms of naturalness and similarity. In addition, the performance of MaskCycleGAN-VC seriously deteriorates because of a limited amount of training data. In order to solve above problems, we propose Pyramid Attention CycleGAN (PACycleGAN) for voice conversion which integrates pyramid structure and attention mechanism. We use a method named differentiable augmentation to improve the data efficiency of GANs and make training more stable. We evaluate the performance of PACycleGAN on inter-gender and intra-gender non-parallel VC. Subjective and objective evaluations in naturalness and speaker similarity show that PACycleGAN-VC outperforms MaskCycleGAN-VC for every VC pair. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://chenpaopao.github.io/chenpaopao/Cyclegan/index.html
PreTracker: Using Heatmap Prediction Combined with Feature to Enhance Tracking

By associating the positions and features of object detection results, tracking by detection framework have performed well in multiple-object tracking (MOT) task. But models of this framework focus on using the consistency of objects' bounding-box and appearance to improve data association ability, ignoring the detection model could also be optimized by temporal information. In this article, we propose a novel MOT model named PreTracker. It associates the heatmaps of the targets in the previous frames to predict the heatmap of the targets in the current frame with an additional heatmap prediction module (HPM) during detection. After that, it combines the predicted heatmap with semantic features to enhance the detection capability, thus improving the tracking performance. After validation on the MOT17 validation dataset, our model obtains a 2.1 % improvement in multiple object tracking accuracy (MOTA) and a 1.7% improvement in ID F1 score (IDF1) based on FairMOT.
Combining Interpretability and Filtering Algorithm for Sequence Recommendation

Sequence recommendation has become more and more important in various online recommendation services. It can obtain the user's dynamic preference characteristics from the user's historical behavior data to predict the next possible interaction item of the user, so as to provide accurate recommendations for the user. Most of the existing models only focus on a single user history interaction sequence, which will lead to the lack of interpretability of the recommendation results. Meanwhile, in the online platform, the recorded user behavior data will inevitably contain noise. In order to solve the above problems, we propose a new method Combining Interpretability and Filtering algorithm for Sequence recommendation (CIFA_Rec). Through multi-attribute modeling, the interpretability of the sequence recommendation model is increased. At the same time, we also borrow the idea of filtering algorithm and use the learning filter to adaptively reduce the data noise. A large number of experiments on three real datasets show that our proposed model has good performance in sequence recommendation and interpretability.
Feature Extraction for Payload Classification: A Byte Pair Encoding Algorithm

Payload classification is a kind of deep packet inspection model that has been proved effective for many Internet applications such as, but not limited to, intrusion detection and network diagnostics. In typical payload classification, feature extraction is the first and very important step which makes a great impact on the quality and quantity of classification results. At present, most feature extraction of payloads adopts n-gram model. However, n-gram model generates features in fixed-length (length of n), which may induce kinds of information loss for feature extraction. In this paper, we propose a very different Byte Pair Encoding (BPE) algorithm for payload feature extractions. In this algorithm, we introduce a novel concept of sub-words to express the payload features, and thereby have the feature length not fixed any more. By the BPE, we can first initialize a vocabulary in a single byte basis, and then continuously update the vocabulary by merging the most frequent byte pairs in the payload to form new sub-words until all sub-word pairs reach the (approximately) same frequency, regardless the lengths of these sub-words. We finally have a very flexible and scalable vocabulary for feature extraction and payload embedding. At the end, we conduct sets of payload classification experiments on the CIC-IDS2017 dataset, in order to verify the effectiveness of our algorithm. The results have successfully confirmed the better classification performance by the use of our BPE algorithm than the traditional n-gram methods.
Research on Data Augmentation for Garbage Classification with Target Detection Algorithm

Garbage classification is an essential work in daily life. With the development of artificial intelligence (AI), we have begun to use object detection to achieve garbage classification. However, the stacking and occlusion of garbage often make the detection performance unsatisfactory. In this paper, YOLOv5 algorithm is used for garbage detection and classification. YOLOv5 has three detection heads, which can detect objects at multiple scales. At the same time, Mosaic and Coarse Dropout image augmentation techniques are used to process our private data set of garbage, which improves the diversity of images. In this way, the target classification accuracy of the detection model can reach 75.7%, which is significantly improved compared with 69.9% without data augmentation. This paper provides a new technical support for the scene classification of stacked garbage based on deep learning.
Blockchain-Based Security Check Framework in the State of the COVID-19 Epidemic

At present, one of the effective ways to deal with the widespread of COVID-19 is to control the source of infection. As the gate of population flow in various places, the security inspection department plays a vital role in screening positive patients in the population; To solve the problems of credibility and lack of human resources, this paper establishes a security check framework based on the blockchain and combines machine learning with the blockchain: the blockchain records the abnormal results of COVID-19 nucleic acid detection and the abnormal conditions detected by the security inspection system (such as no mask, high temperature); Use machine learning to realize mask recognition and other functions. The architecture, data flow, and key elements are presented and discussed. The study findings could solve the security problem under the epidemic and provide relevant enlightenment for the effective combination and application of machine learning and blockchain.
Pedestrian Trajectory Extraction Method Based on UAV Video

With the development of UAV technology, pedestrian trajectory extraction based on UAV video plays an increasingly prominent role in public safety. Aiming at the problems of small pedestrian targets in UAV video and the effect of pedestrian detection and tracking is easily blocked by obstacles in the scene, this paper analyzes the multi-target tracking algorithm framework based on detection, takes YOLOv5 as the target detection model, and gets a better anchor frame through KMeans++ clustering method. At the same time, CBAM attention module is integrated into YOLOv5 network to realize the effective extraction of small target features; With Deep_SORT is a target tracking model. By calculating the confidence of the trajectory, an improved correlation matching tracking algorithm framework is proposed to solve the problems of trajectory fracture caused by pedestrians being blocked, and provide a strong guarantee for extracting complete and reliable pedestrian trajectory information in UAV video. The mAP of this paper is 49.1 % on the VisDrone2019-DET dataset, and the MOTA is 48.0% on the VisDrone2019-MOT dataset. Experiments show that the pedestrian trajectory extraction method in this paper can extract more stable and continuous pedestrian trajectory.
Multi-modal Image Registration Based on Adaptive PIIFD

In recent years, due to the wide application of multi-sensor vision systems, multi-modal image acquisition technology has continued to develop. Most of the existing multi-modal image registration methods are only suitable for two modalities, and cannot uniformly register multiple modal image data. Therefore, our method proposes a multi-modal image registration method based on adaptive multi-scale PIIFD. This method extracts KAZE features, then adaptive multi-scale PIIFD is calculated for matching. Finally, the mismatch is removed through the consistency of the feature main direction, and the image alignment transformation is realized. The quantitative comparisons with other three advanced methods show that our method can achieve excellent performance in multi-modal image registration.
YOLOv3-XH: More Accurate Object Detection Using Feature Enhancement and Fusion

Object detection is an important basis for understanding the high-level semantic information of images. To address the problems of small object accuracy and inaccurate bounding box localization in the YOLOv3, we propose a new object detection method, YOLOv3-XH. In YOLOv3-XH, we make three improvements to YOLOv3. Firstly, we propose the Enhanced Feature Pyramid Network (EFPN), which fuses Path Aggregation Network (PAN) and Feature Pyramid Network (FPN) and helps extract richer feature information and increase the accuracy of small object detection. Secondly, the Multi-Branch Receptive Field (MBRF) is proposed, which adds a branch to RFB for feature extraction and helps to increase the receptive field and enhance feature extraction. Finally, a new loss function is proposed, using GIo U loss as the bounding box loss function, so that the network is optimised in the direction of a higher overlap between the predicted and real boxes. Our approach achieved an average mean accuracy mAP of 83.02% (IoU=O.5) on the PASCAL VOC test set, reaching an improvement of 1.83%.
A Survey of Adversarial Attacks on Deep Neural Network in Computer Vision

As an important part of artificial intelligence, deep learning has been widely used in computer vision. However, since deep neural networks lack of theoretical support, their security has been questioned by lots of researchers. Recent studies have shown that deep neural networks are susceptible to subtle perturbation. Researchers call the method of using perturbation to mislead the model adversarial attack. In this paper, we introduce some basic concepts of adversarial attacks in the computer vision field and analyze the causes of adversarial examples. We describe and compare some attack methods. Finally, we review the development of adversarial attack technology in computer vision and prospect potential development directions in this field.
An Offensive Language Identification Based on Deep Semantic Feature Fusion

Various forms of social interactions are often char-acterized by toxic or offensive words that can be collectively referred to as offensive languages, which has become a unique linguistic phenomenon in social media platforms. How to detect and identify these offensive languages in social media platforms has become one of the important research in the field of natural language processing. Existing methods utilize machine learning algorithms or text representation models based on deep learning to learn the features of offensive languages and identify them, which have achieved good performances. However, traditional machine learning-based methods mainly rely on keyword identi-fication and blocking, deep learning-based methods do not ade-quately explore the fused deep semantic features of the content by combining word-level embeddings and sentence-level deep semantic feature representations of sentences, which cannot ef-fectively identify offensive languages that do not contain common offensive words but indicate offensive meanings. In this research, we propose a novel offensive language identification model based on deep semantic feature fusion, which uses the pre-trained model Bert to obtain word-level embedding representations of offensive languages, and then integrates the RCNN that combines with the attention mechanism to extract the fused deep semantic feature representations of offensive languages, and label encoder and offensive predictor to improve the identification accuracy and generalization ability of the model so that the performances of the model do not rely on the offensive language lexicon entirely and can identify offensive languages that do not contain common offensive words but indicate offensive meanings. Experimental results on Wikipedia and Twitter comment datasets show that our proposed model can better understand the context and discover potential offensive meanings, and outperforms existing methods.
DeepHoldem: An Efficient End-to-End Texas Hold'em Artificial Intelligence Fusion of Algorithmic Game Theory and Game Information

Texas Hold'em has received considerable attention from researchers as a typical representative of imperfect information games. In the last 5 years, Texas Hold'em artificial intelligence (AI), which adopts algorithmic game theory as its core technology, has achieved many breakthroughs, with Libratus, DeepStack, Pluribus, and other Texas Hold'em AIs achieving the ability to beat top human players in heads-up and multiplayer no-limit Texas Hold'em. In the past 2 years, researchers have begun exploring the use of neural networks to build decision-making models to reduce the dependence of model training on expert knowledge. AlphaHoldem is an essential representative of these neural networks, beating Slumbot through end-to-end neural networks. However, AlphaHoldem does not fully consider game rules and other game information, and thus, the model's training relies on a large number of sampling and massive samples, making its training process considerably complicated. In this study, we propose DeepHoldem, an efficient end-to-end Texas Hold'em AI that combines algorithmic game theory and game information. DeepHoldem uses reinforcement learning to simulate the self-play process of the counterfactual regret minimization algorithm. DeepHoldem can efficiently converge to the Nash equilibrium by considering player and opponent hand information when encoding the game state and incorporating the attention module into the network structure to achieve the correlation extraction of game action sequences, and thus, fully utilize game state information. In addition, DeepHoldem incorporates the concept of supervised learning into the training process, dramatically improving the training efficiency of the model and expanding its application scope. Experimental results show that after about 4 days of training, DeepHoldem beats Slumbot at a level of 2.1 mbb/h, and the single decision time is less than 0.1 seconds.
RF-Based on Feature Fusion and Convolutional Neural Network Classification of UAVs

RF fingerprint identification is an identification technology that relies on the hardware characteristics of wireless devices. This paper proposes a UAV RF recognition scheme based on convolutional neural network and feature fusion of time-frequency spectrogram and bispectrogram. Firstly, we obtain the frequency spectrogram and bispectrogram characterizing the UAV RF signal, and then classify them by designing a two-way convolutional neural network after feature fusion in the fully connected layer, and the classification accuracy reaches 99.47%. Under the condition of low SNR, the classification accuracy of the model reaches 88.63% with good classification performance through the denoising mechanism.
Construction of Military Knowledge Graph Based on Neo4j and MongoDB

In the future battlefield, how to dig out accurate and useful information from the battlefield, judge the potential connotation of target events and eliminate the “fog of war” is the key to winning the war. With the development of information technology, the sources of battlefield intelligence information are becoming more and more extensive, and problems such as excessive information, difficulty to distinguish true from false and different forms have emerged one after another. In order to solve the above problems, this paper constructs a panoramic military knowledge graph by analyzing the information char-acteristics of the military field and unifying the expression of massive multi-source heterogeneous military information, which not only realizes the mining and accumulation of knowledge in intelligence information but also establishes the association and fusion of multi-source heterogeneous intelligence information at the semantic level. In addition, the military knowledge graph is stored in Neo4j and MongoDB to provide an intuitive and applied representation of knowledge, respectively. Finally, proposing the application scenarios and future development directions based on the military knowledge graph are given.