Reviewerly/exHarmony huggingface dataset:
Less is More: Mitigating Tor Traffic Correlation with Distance-Aware Path Selection

Traffic correlation attacks can deanonymize Tor users by capturing and analyzing traffic patterns on two end paths, i.e., the path from a client to the anonymity network and the path from the anonymity network to the destination server. Prior studies propose to obtain the two corresponding routes to ensure that no autonomous system (AS) can simultaneously appear on both end paths. However, acquiring accurate AS-level Tor paths in time is challenging due to the inaccuracy of route inference techniques, a shortage of traceroute vantage points, and the inherent dynamics of Internet routing. Moreover, it will fail to infer correct AS-level routes due to the widely deployed content delivery networks (CDNs). In this paper, we present a distance-aware Tor path selection algorithm that mitigates traffic correlation attacks merely by constraining the distances between Tor path nodes. The algorithm aims to lower the probability of traffic correlation attacks while not requiring any prior knowledge of AS-level route paths. The simulation results demonstrate that the distance-aware algorithm reduces the risk of Tor traffic correlation attacks by up to 27% compared to the existing AS-aware algorithm and outperforms it in about 88% of cases.
Three-Channel Graph Convolutional Network

At present, as an emerging direction of artificial intelligence, graph neural networks (GNNs) have developed rapidly and can solve many graph data problems efficiently. The real world is composed of a large number of networks. Compared with the widely studied single-relationship feature information networks, the real-world networks are mostly composed of multi-relationship feature information networks in which different types of objects are interconnected through complex relationships. Complex structural information and rich semantic information are included by multi-relationship feature information networks, thereby the real network can be restored more realistically. However, one type of relational feature of the network is merely considered by most of the existing GNNs, and the information of multi-relationship features and the hidden information between multi-relationship features are ignored in the network. In order to deal with these restrictions, a novel graph convolutional network (TCGCN) based on an attention mechanism is proposed, which aims to extract different relational features in multi-relationship feature networks and the hidden information between them more comprehensively, thereby improving the performance of GNNs in downstream tasks. In this paper, an improved attention mechanism is used to aggregate different types of single relational features, and the aggregated multiple single relational feature network information is fused with the mixed relational feature network information into the final node representation. The proposed TCGCN outperforms most other methods in extensive experiments conducted on three real-world datasets.
CampusTrace: A Framework for Centralized Contact Tracing System for Universities in Preparation for Face-to-Face Classes

The government addresses that one of the biggest problems in the country is lacking an effective contact tracing solution. The Philippines' current contact tracing systems have encountered a lot of challenges because of the lack of features that would ensure safety and awareness to users around. The study aims to propose a system framework that can be used as Contact Tracing Solution using data warehousing and edge computing would improve the tracing in small and concentrated areas such as universities and offices. The researchers gather reviews and studies on how to develop a system that would address the current problem in the contact tracing scenario in the Philippines, particularly in the education field. The researcher will be going to apply the descriptive and development design for the conduct of the study and the waterfall methodology will be the software model for the development of the proposed system. Therefore, it is better to develop a contact tracing application that will be used by universities whose main objective is to spread awareness to potentially close contacts of a COVID-19 positive case and further implement the system framework to provide a proactive solution for contact tracing in the academe.
Automatic Extraction Algorithm of English-Chinese Translation Template Based on Cross-cultural Perspective

Using computer to translate language automatically is the product of Internet. This technology makes the communication of various languages in the world more convenient and accurate. However, there are difficulties in understanding and expressing human language in MT. The research on MT has a long history, but it has not yet fully achieved the goal expected by human beings. With the rapid development of computer software and hardware technology and the improvement of corpus construction, MT using statistical knowledge becomes possible, and the translation quality is expected to be one step closer to human expectations. This article mainly discusses the current MT from the perspective of language and culture, and points out the obstacles to correct grammatical analysis in the translation process. Language and culture comparison is conducive to the understanding and use of foreign languages and cultures.
Binary Gaussian Mask Weighted Joint Dynamic and Static Guided Discriminative Correlation Filter for Visual Tracking

In order to relieve the pressure of urban transportation system, intelligent transportation will become the development trend of future transportation, and multi-target detection and tracking will become an indispensable part of intelligent transportation system. However, the detection and tracking algorithm in the current system has problems of tracking target partial occlusion, target deformation, light changes, etc. To address these problems, a binary Gaussian mask weighted joint motion guidance method is proposed in this paper. The method weights the global response of the target to maintain the central global response, thus avoiding the interference of discontinuous boundary gradients. In addition, we exploit the difference between the input characteristics of pre- and post-frame still images and the dynamic structure of the filter. By jointly guiding the update of the correlation filter, the impact of target distortion on the accuracy of the model update is reduced. Extensive experimental results fully demonstrate the great potential and performance of the method in solving visual tracking problems.
RTD: The Road to Encrypted Video Traffic Identification in the Heterogeneous Network Environments

The explosive growth of video traffic brings challenges to network management and optimization. It is important to identify video traffic to realize effective network resource management and quality of service (QoS) provisioning. With the popularity of traffic encryption, most traffic identification methods use the following types of features: statistical values or sequences of packets length and packets interval, header fields of the transport layer and its underlying protocols, features of TLS handshake protocol or raw binary data directly. Numerous studies have shown that video traffic can be identified in scenarios where the traffic is encrypted. However, most current methods do not generalize well enough in unknown heterogeneous network environments. In this paper, we propose an innovative feature, namely Response Time Distribution (RTD). We subtly extract the response time of the server and client, which remains relatively stable even in network scenarios with different network qualities, as the original input features, and select the popular tree model with excellent performance as the classification model. The results of an extensive experimental study on several open datasets and real-world traffic datasets show the proposed method is sufficient to resist the complexity of an unknown heterogeneous network environment. In the case that the F1 of the state-of- art-solutions drops by more than 20 %, the F1 of the proposed method still maintains more than 92 %.
Communication-Efficient Federated Learning Based on Chained Secure Multiparty Computing and Compressed Sensing

Federal learning (FL), as a new technology with privacy protection capabilities, has been widely used. However, the protection capabilities of FL are limited and a lot of communication overhead is required, which is often limited by communication environments. In order to improve the privacy protection and reduce the communication burden, we combine secure multi-party computing(SMC) and compressed sensing(CS) to propose a new chained federated learning scheme called Chain-FL via CS (Chain-FL-CS). We first design a heuristic compression transmission strategy for the model based on CS so that the model can be compressed with a lower loss. Then we use a chained-SMC to perform lossless and secure aggregation of the calculation results. This technology mainly uses masking mechanism for communication. Compared with other schemes, ours has lower complexity. We have conducted experiments using a Deep Neural Network Model (DNN). Experimental results show that our scheme can guarantee the convergence of model under the premise of high transmission efficiency, and it has certain privacy protection capability.
A Few-Shot Image Classification Method Based on Information Entropy for First-Order Gradient Meta-Learning

Due to the lack of samples, few-shot learning is becoming a challenging attempt. However, the overfitting and pool generalization ability caused by the difficulty in obtaining enough task features and others difficulties constrained the application of few-shot learning. In this paper, we proposed an improved meta-learning algorithm based on Reptile algorithm. In the improvement, the feature extractor was optimized by group convolution and residual connection, which reduced the parameters and the training costs. The information entropy task-independent regularization term was introduced to minimize the inequality of task performance and reduced the training over-fitting of the meta model. The advantages of our improvement were verified by two real-world datasets. It was found that the test accuracies of the improved meta learning algorithm and meta model on the 5-way 1-shot and 5-way 5-shot tasks of the mini-ImageNet was higher than that of orignal Reptile algorithm about 6.3% and 5.9%, while about 13.2% and 14.3% than that of orignal Reptile algorithm on CUB-200-2011 dataset, respectively.
A Clustering Resampling Stacked Ensemble Method for Imbalance Classification Problem

The results of the existing research on ensemble methods based on resampling are the best for the imbalance classification task, whereas the results of independent use of resampling or ensemble learning are relatively mediocre. Compared with the other two ensemble strategies such as boosting and bagging, stacking is often better than iterative boosting in training speed because it is an ensemble strategy that parallels base classifiers. Bagging uses a weighted average at the decision level, whereas stacking uses a machine learning model to make decisions, which achieves higher accuracy than bagging using a weighted average decision. The effect of stacking relies heavily on the variance of the base classifiers, and it is often better to ensure that the base classifiers have good learning performance while making the variance among base classifiers as large as possible. The key to imbalanced classification is to deal with the class imbalance and overlapping in training sets. In this study, we will use a hierarchical approach to solve these two problems. Thus, we propose a resampling-based stacking ensemble method combined with clustering. The proposed method uses a support vector machine as the base classifier to solve the problem of losing important information and easily introducing noise in the resampling process through clustering. It also increases the differences among base classifiers. We use the proposed stacking ensemble method to hierarchically solve the class overlapping and then use the cost-sensitive algorithm to solve the class imbalance. The statistical significance analysis of the experimental results shows that the proposed method performs better than existing imbalanced ensemble methods.
A Robust Watermarking Algorithm for Exchange Ciphers Using Homomorphic Encryption

With the continuous development of cloud technology, multimedia information can be stored and calculated on the cloud server, bringing convenience and privacy leakage. In order to protect the copyright of digital images, a robust watermarking algorithm based on homomorphic encryption in a cloud environment was proposed by combining homomorphic encryption technology with digital watermarking technology. Firstly, the user uses the Paillier encryption system to encrypt the original image and the watermark information related to the user's identity to obtain the encryption image and encryption watermark, which are uploaded to the cloud server for watermark embedding; In the encryption domain, the non-standard wavelet S-transform and the homomorphism of Paillier encryption system are used to embed the encryption watermark. In the image retrieval stage, users can directly extract the watermark in the encryption domain or extract the plaintext watermark information from the decrypted plaintext image with the watermark. The experimental results show that the decrypted image with a watermark has good transparency, and the embedded watermark has strong robustness to common image processing operations.
Research on Path Planning of Welding Robot Based on Ant Colony Algorithm

In the welding process of large and complex components, the welding gun must go through all the welds to complete the work, and the working path of the welding gun is often not unique. Finding a path that satisfies the welding process constraints and is as short as possible in these paths can significantly improve the welding efficiency. In view of the traditional ant colony algorithm in path planning problems, due to factors such as terrain environment, the planned path is not practical, and when faced with large-scale problems, the traditional ant colony algorithm often reflects the slow convergence speed, easy to fall into local optimum, and it is difficult to jump out of local optimum. The shortcomings of the proposed welding robot path planning based on improved ant colony algorithm. In this paper, the grid method is used to segment the environment and identify the obstacles in the program in the form of 0 / 1 matrix, so that the ant colony in the algorithm can have the ability to avoid obstacles. By improving the roulette method in the traditional ant colony algorithm, the next movement scheme searched by the roulette method is not limited to the range of nine grids, and the geometric judgment of obstacles, target points and starting points makes the path searched by the algorithm more direct. Through Matlab simulation experiments, the improved ant colony algorithm can plan a better global path more quickly. It can avoid the robot falling into local optimum, and ensure that the robot avoids obstacles while planning the shortest path.
Mastering First-person Shooter Game with Imitation Learning

Imitation learning has been heavily studied as a technique for solving sparse-rewarding sequential decision problems such as video games and robotics. Previous research has mostly focused on training agents with large-scale behavioural cloning. However, behavioural cloning can perform poorly if the size of the dataset is not big enough, while large-scale labelled human demonstrations are usually unavailable. We propose a new imitation learning framework called Imitational Actor-Critic (IAC), which unifies behavioural cloning and actor-critic based reinforcement learning. Specifically, we show that with a small amount of human demonstrations we can train an agent that masters the first-person shooter (FPS) game. Our model obtains performance gains over behavioural cloning counterparts on hard-exploration tasks that are impossible to learn from scratch via reinforcement learning.
Machine Learning-enabled Performance Model for DNN Applications and AI Accelerator

As innovations in deep learning systems and deep neural network (DNN) models continue to grow, accurate performance analysis acts as a promising tool for understanding and navigating the complex software-hardware interplay, especially for the today's heterogeneous AI architecture. However, the actual execution of DNNs on the dedicated accelerators involves chal-lenges from nontrivial dataflow graph analysis, tensor compiler optimizations, and operator performance prediction. In this work, we propose a two-stage performance model framework that combines graph-level analysis and operator-based hotspot modeling to bridge the gap between high-level application performance and its software-hardware systems. By the employ of machine learning (ML) solution, our performance model further captures the low-level hardware-dependent information, including operator fusion and data layout transformation. Our graph analysis for mainstream models from computer vision (CV), natural language processing (NLP) and recommendation domains selects total 26 kinds of operators and builds a dataset on the Huawei Ascend 910. With the well-trained model, our open source <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> Source code available at https://github.com/Huawei-Performance-Model/Ascend-910b performance model finally achieves 15.4 % average error for predicting the execution time of DNN models, and our modeling for memory access and performance bottleneck supports efficient running of DNN models for future systems.
PC-BNA: Parallel Convolution Binary Neural Network Accelerator

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. Therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). Based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. The input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. The experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. The image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. The results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications.
TianheQueries: Ultra-Fast and Scalable Graph Queries on Tianhe Supercomputer

As graph-based services have been widespreadly integrated into daily lives, extremely-fast and scalable graph queries are receiving increasing attention. Graph querying al-gorithms including BFS(Breadth First Search) and SSSP(Single Source Shortest Path) are promising engines for graph-based applications. Unfortunately, current graph queries often deliver low efficiency owing to sequential execution and poor scalability. This paper presents TianheQueries: an ultra-fast and scalable graph querying engine on Tianhe supercomputer. TianheQueries proposes an efficient hardware prefetching and caching mechanism that can pipeline vertices gather, traversal and vertices scatter so as to accelerate graph queries with the utilization of pipeline parallelism. and (ii) presents a topology-aware communication aggregation for inter-domain messages more than from the same source node by leveraging the network topology information to perform more aggressively messages aggregation. We use both benchmark and real-world graphs to demonstrate the effectiveness of TianheQueries. Specially, TianheQueries helps Tianhe Exa-node win the top spot of the latest Green-Graph500 lists both big data and small data categories. Further-more, TianheQueries-based SSSP tests on Tianhe supercomputer is superior to the fastest SSSP systems in the latest Graph500 lists.
LightSCA: Lightweight Side-Channel Attack via Discrete Cosine Transform and Residual Networks

Side-channel attacks (SCAs) crack the keys through physical signals that are unintentionally leaked during the operation of cryptographic devices. This poses a severe threat to users' privacy and network security. Deep learning (DL)-based SCAs have become one of the most important means of evaluating the security of cryptographic devices. However, the existing SCAs still have the following limitations. First, the existing SCAs based on convolutional neural networks (CNNs) have large networks sizes and take a long time to train the model. Second, sensitivity to desynchronized signals is also a significant problem. For desynchronized signals, the side channel traces required to crack the key increase rapidly compared to the synchronized signals. In this background, we analyze these problems in detail and aim to design a SCA algorithm that is lightweight and robust to desynchronized signals. In this paper, we propose a Lightweight Side-Channel Attack model, named LightSCA, based on discrete cosine transform (DCT) and residual networks. We use the DCT to extract the pivotal features of the side channel traces. Specifically, the feature length is compressed to 1/35 of the original. In order to improve the efficiency of model training and key cracking, we design a multi-classifier based on residual networks. The number of LightSCA network parameters is less than 1/100 of the CNNbest model. We validate the performance of LightSCA on the public ASCAD dataset. Compared with four representative DLSCA models, the proposed model achieves better effectiveness. For the synchronization signals, LightSCA cracks a key byte using only 87 traces.
CCT Online Request of Students Credentials: A Document Management System for Private HIEs in the Philippines

At a start of an institution, manual requisition of documents and credentials can be quick, easy, and necessary. However, as your institution grows, the manual requisition can be a very cumbersome practice. In any busy institution, it is important that each accomplished student's request must be quick and effective. More efficient ways in today's growing technological world can help keep you organized with higher productivity. Since we are facing a worldwide pandemic, it is wiser to have a system that provides an online requisition of credentials. The goal is to develop an online request for student credentials for a Higher Education Institution in the Philippines, where the City College of Tagaytay (CCT) is the case site of the study. An Agile software methodology was used in the development of the system. The system was initially implemented in the CCT. Given the initial implementation of the system, it shows that it is more convenient and safer for the students to request student credentials. The importance of the system was highlighted during the pandemic, and other features of the system will be included to cater to other HIEs requirements.