Reviewerly/exHarmony huggingface dataset:
Mixup Training for Generative Models to Defend Membership Inference Attacks

With the popularity of machine learning, it has been a growing concern on the trained model revealing the private information of the training data. Membership inference attack (MIA) poses one of the threats by inferring whether a given sample participates in the training of the target model. Although MIA has been widely studied for discriminative models, for generative models, neither it nor its defense is extensively investigated. In this work, we propose a mixup training method for generative adversarial networks (GANs) as a defense against MIAs. Specifically, the original training data is replaced with their interpolations so that GANs would never overfit the original data. The intriguing part is an analysis from the hypothesis test perspective to theoretically prove our method could mitigate the AUC of the strongest likelihood ratio attack. Experimental results support that mixup training successfully defends the state-of-the-art MIAs for generative models, yet without model performance degradation or any additional training efforts, showing great promise to be deployed in practice.
LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions.
SJA: Server-driven Joint Adaptation of Loss and Bitrate for Multi-Party Realtime Video Streaming

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing significant challenges to service providing. First, unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as bitrate and loss.In this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based relaxation and the SJA algorithm are further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ∼ 46.5%.
Federated Learning with Flexible Control

Federated learning (FL) enables distributed model training from local data collected by users. In distributed systems with constrained resources and potentially high dynamics, e.g., mobile edge networks, the efficiency of FL is an important problem. Existing works have separately considered different configurations to make FL more efficient, such as infrequent transmission of model updates, client subsampling, and compression of update vectors. However, an important open problem is how to jointly apply and tune these control knobs in a single FL algorithm, to achieve the best performance by allowing a high degree of freedom in control decisions. In this paper, we address this problem and propose FlexFL – an FL algorithm with multiple options that can be adjusted flexibly. Our FlexFL algorithm allows both arbitrary rates of local computation at clients and arbitrary amounts of communication between clients and the server, making both the computation and communication resource consumption adjustable. We prove a convergence upper bound of this algorithm. Based on this result, we further propose a stochastic optimization formulation and algorithm to determine the control decisions that (approximately) minimize the convergence bound, while conforming to constraints related to resource consumption. The advantage of our approach is also verified using experiments.
Mercury: Fast Transaction Broadcast in High Performance Blockchain Systems

Blockchain systems must be secure and offer high performance. These systems rely on transaction broadcast mechanisms to provide both of these features. Unfortunately, in today’s systems, the broadcast mechanisms are highly inefficient.We present Mercury, a new transaction broadcast protocol designed for high performance blockchains. Mercury shortens the transaction propagation delay using two techniques: a virtual coordinate system and an early outburst strategy. Simulation results show that Mercury outperforms prior propagation schemes and decreases overall propagation latency by up to 44%. When implemented in Conflux, an open-source high-throughput blockchain system, Mercury reduces transaction propagation latency by over 50% with less than 5% bandwidth overhead.
Privacy as a Resource in Differentially Private Federated Learning

Differential privacy (DP) enables model training with a guaranteed bound on privacy leakage, therefore is widely adopted in federated learning (FL) to protect the model update. However, each DP-enhanced FL job accumulates privacy leakage, which necessitates a unified platform to enforce a global privacy budget for each dataset owned by users. In this work, we present a novel DP-enhanced FL platform that treats privacy as a resource and schedules multiple FL jobs across sensitive data. It first introduces a novel notion of device-time blocks for distributed data streams. Such data abstraction enables fine-grained privacy consumption composition across multiple FL jobs. Regarding the non-replenishable nature of the privacy resource (that differs it from traditional hardware resources like CPU and memory), it further employs an allocation-then-recycle scheduling algorithm. Its key idea is to first allocate an estimated upper-bound privacy budget for each arrived FL job, and then progressively recycle the unused budget as training goes on to serve further FL jobs. Extensive experiments show that our platform is able to deliver up to 2.1× as many completed jobs while reducing the violation rate by up to 55.2% under limited privacy budget constraint.
AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization

Traditional deep learning compilers rely on heuristics for subgraph generation, which impose extra constraints on graph optimization, e.g., each subgraph can only contain at most one complex operator. In this paper, we propose AGO, a framework for graph optimization with arbitrary structures to boost the inference performance of deep models by removing such constraints. To create new optimization opportunities for complicated subgraphs, we propose intensive operator fusion, which effectively stitches multiple complex operators together for better performance. Further, we design a graph partitioning scheme that allows an arbitrary structure for each subgraph while guaranteeing the acyclic property among all generated subgraphs. Additionally, to enable efficient performance tuning for complicated subgraphs, we devise a divide-and-conquer tuning mechanism to orchestrate different system components. Through extensive experiments on various neural networks and mobile devices, we show that our system can improve the inference performance by up to 3.3× when compared with state-of-the-art vendor libraries and deep compilers.
A Fintech Platform Using Blockchain Smart Contract

Though few blockchain-based payment services are currently available, this is expected to change in 2018, as investment has poured in from banks to explore blockchain’s potential. This creates the potential for developing live blockchain payment processing solutions and trade finance deployments. However, current blockchain technology is unsuitable for real-world applications owing to various limitations. Bitcoin is simply a “virtual” currency or “cyber” money because blockchain does not support owner identification. This study combines credit card payments and a blockchain network to overcome this limitation. If there is no connection between the credit card payment system and the blockchain network, blockchain ciphers like Bitcoin will remain a “virtual” currency or “cyber” money forever. This paper presents a challenging study involving blockchain and financial technology (fintech). Furthermore, we must consider the integration approach in terms of performance. Even the performance of state-of-the-art blockchain platforms cannot meet fintech application standards in the real world. In order to resolve performance issues related to a blockchain network while processing credit card transactions, we exploit the overlay network concept to separate the credit card network from the relatively slow blockchain peer-to-peer (P2P) network. In this paper, we presented the details of the data preparation, assessment metrics, and evaluation of our method. We also described the experimental results for the fintech platform using blockchain smart contracts.
Teaching Strategy Research on Project-based Collaborative Learning

In the era of "Internet+", the cultivation of innovative talents with practical ability for the society is the focus of the research of educators. Project teaching is based on cultivating students' comprehensive practical ability as the starting point,and the core concepts to be taught to students as the basis of the course.Students are the main body of all the teaching activities, and teachers are the guide of all the teaching activities, The entire teaching process is carried out in continuous exploration, practice, collaboration, and reflection. With the rapid development of project teaching, although it has a certain practical role in the teaching field. However, there are still some objective problems, such as lack of teaching resources, heavy tasks, and insufficient teaching time.In response to this, this study uses project teaching as the starting point, and uses education technical methods to combine multiple education, teaching, and learning theories to carry out empirical research in hybrid collaborative learning, and build a mixed collaborative learning teaching strategy based on project teaching, and It was applied in "Digital Electronic Technology and Practice" in in the computer science professional course.
Structural Deep Graph Network with Adjacency Matrix Alignment

Recently, deep clustering methods have become a hot research topic, as they fuse deep learning and clustering by utilizing deep neural networks like autoencoders to learn effective feature representations from data. SDCN (Structured Deep Clustering Network) is a GCN-based deep clustering method that combines the data features learned by autoencoders with graph neural network to achieve better performance of the node clustering task. However, we find that SDCN only utilize the feature information, but not consider the adjacency matrix information. Thus we propose the SDAA method, which further enhances clustering performance by aligning the similarity matrix learned by the autoencoder with the adjacency matrix in the graph structure. We conduct node clustering experiments on two real-world datasets to demonstrate the performance of our method.
Study on the Recent Development of Metaverse Application in College Teaching

This paper mainly investigates the application of metaverse in college teaching. Firstly, we compare the understanding and expression of the concept, characteristics and supporting technology of metaverse in the research results from different scholars. Considering that there is no authoritative definition of the metaverse, the characteristics and supporting technologies recognized by many studies are summarized. The purpose is to find an indirect consensus on the concept of the metaverse. Secondly, the existing literatures related to metaverse teaching is extracted and sorted out from different angles, including general theoretical research, application exploration research and practical progress. We also point out the existing problems such as more theoretical research than practical application, lack of peer cooperation among scholars and institutions, and wide application difficulties because of practical obstacles. This paper holds that although metaverse teaching is still in its infancy, it has a very broad development prospect because it has greatly broken through the limitations of traditional blended teaching or online teaching in terms of teaching resources, teaching environment and teaching mode. Future research should focus on solving the current predicaments in legal, ethical, technical, cost and other detailed aspects, as well as appropriate forward-looking researches on the teaching of the next generation of metaverse.
A Novel Hybrid Machine Learning Framework to Recommend E-Commerce Products

E-Commerce is the activity of electronically purchasing or selling products in an online platform. E-Commerce recommender systems provide suggestions of products based on the consumer sentiment and ratings. There is often a mismatch between consumer rating and their sentiment. Identifying the accuracy of the mismatch is a challenge in machine learning. This research proposes a Novel Hybrid Machine Learning Framework to Recommend E-Commerce Products based on consumer sentiment and product descriptions. This proposed framework combines a text embeddings model, sentiment analysis model and a rating engine. The text embeddings model is implemented using gensim doc2vec for consumer reviews and product descriptions. Further it uses neural networks for capturing the consumer product interactions for collaborative filtering. The sentiment analysis model is implemented by inputting distributed text embeddings into neural networks that are trained to capture content feature of products and sentiment of consumer evaluations. The rating engine is implemented by aggregating several embeddings as attention weights for consumers and products, then outputting the prediction score for the consumer–product interaction. This research makes use of the real-world Amazon product category semi structured baby and digital music semi structured datasets, each of which contains information on consumer reviews and product metadata. Mean absolute error (MAE) and root mean-square error (RMSE) are considered to evaluate the recommendation performance, thereby measuring the accuracy of prediction ratings. Experimental results on Amazon distinct product dataset demonstrate an accuracy metric MAE value of 0.5909 and RMSE value of 0.8080. These results demonstrate that the proposed framework performs better on rating prediction in enhancing consumers experience in order to find their preferences for e-commerce products. Consequently, e-commerce platforms can enhance sales and consumer satisfaction by using machine learning frameworks to recommend which products a consumer will be interested in based on their past purchasing behavior.
MacBERT classification model of memory attention mechanism and its application to the power system of EAST neutral beam injection facility

To provide researcher and operation personnel with recommendations on the condition of equipment so as to ensure the safe, reliable, and smooth operation of the EAST-NBI (Experimental Advanced Superconducting Tokamak Neutral Beam Injection) power system, we propose a memory attention mechanism MacBERT (MLM as correction Bidirectional Encoder Representation from Transformers) text classification model. Firstly, we use MacBERT to generate word vectors containing context, which alleviates the masking differences in pre-training and fine-tuning phases. Secondly, the deep features are further extracted and important parts are highlighted through the memory attention module. Finally, the Linear layer and Softmax layer are used to find the label with the highest predicted probability. Experimental results show that the proposed model performs well in the classification of maintenance records for the EAST-NBI power system, which shows certain research significance.
Design and Development of a Knowledge Service Platform in the Field of Computer Science

This paper introduces a crowdsourcing-based method for expanding knowledge graphs, aiming to address the high production cost problem of building high-quality domain knowledge graphs. The method includes stages such as task assignment, task division, and incentive mechanisms, which leverage the power of crowdsourcing workers to obtain high-quality domain knowledge content and expand and refine the structure and content of knowledge graphs. Additionally, to address the problem that traditional computer teaching methods cannot meet students' learning needs, this paper also develops a computer domain knowledge service platform that is easy to read and understand, has multiple types of knowledge expression, and is interactive. The platform presents the structure and content of knowledge graphs in a visual and interactive way, providing a new way of learning computer knowledge for students and relevant personnel. It not only helps students and relevant personnel better understand computer knowledge but also expands the structure and content of knowledge graphs, providing support for the application and promotion of knowledge graphs. The method and platform presented in this paper have practical value and promotion significance and are expected to play an important role in the expansion and teaching of computer domain knowledge graphs.
Research on Multi-path Quantum Key Distribution Scheme without Public Nodes Based on Trust Relaying

ABSTRACT. Quantum key distribution (QKD) network develops rapidly in recent years. In order to improve the efficiency and security of quantum key distribution, this paper analyzes the advantages and disadvantages of single-path and multi-path quantum key distribution and proposes a multi-path quantum key distribution scheme. This scheme includes two aspects which are path equalization priority strategy and the No-public nodes strategy. The priority of the chosen path is based on the path bandwidth utilization, the number of routing hops and quantum keys stored in the trusted nodes. The no-public nodes strategy selects multiple key distribution paths without public nodes. The scheme can reduce the risk of path blocking and the consumption of quantum keys, improve the efficiency and security of QKD. Finally, this scheme is analyzed and verified.
Learning with Balanced Criss-Cross Attention for Cross-Modality Crowd Counting

Cross-modality crowd counting is one of the most essential tasks in multimedia and image processing, which usually uses multi-sensor information as input in neural networks. Various approaches have been proposed to extract the alignment and relationships between the different modalities in the task of crowd counting. In this work, we explore how to further remedy the cross-modal discrepancies and learn latent relevance across different modalities. We present a novel RGBT crowd counting framework, namely Balanced Criss-Cross Attention Network (BCANet), to overcome the above limitations. To bridge the two modalities, we introduce a Balanced Criss-Cross Attention (BCA) module to encode complementary information across modalities. Lastly, we evaluate our BCANet via extensive experiments and demonstrate that it consistently achieves state-of-the-art results on RGBT-CC and DroneRGBT datasets.
Teaching Pre-editing for Chinese-to-English MT: An Experiment with Controlled Chinese Rules

This paper aims to explore whether the training in pre-editing based on controlled Chinese rules can help students better cooperate with machine translation (MT) to deliver more accurate and effective English MT output. Three main controlled Chinese rules are put forward for web-based product description text. One rule, which requires removing redundant expressions, deals with extra-linguistic aspect. The other two rules, which require adding missing subjects and making sentences short, deal with linguistic aspect. Fifty-one students were trained in the controlled Chinese rules and then participated in a test to evaluate their grasp of relevant pre-editing methods. The results show that 39% of students can significantly improve Chinese-to-English MT output by pre-editing and over 90% of them believe pre-editing based on controlled Chinese rules can help them improve MT quality and translation efficiency. However, students are still deeply influenced by the Chinese thinking pattern and writing customs. They find it hard to determine which parts are redundant; what kind of subject to add and where to add such subjects; and how to divide long Chinese sentences. Students need more time and more practice to grasp the differences in sentence structure between Chinese and English, to master the differences in writing style between Chinese and English business texts, so as to better leverage controlled Chinese rules in pre-editing. This teaching experiment on pre-editing based on controlled Chinese rules for C-E translation is a new attempt and can provide insights for future C-E translation pre-editing teaching. With the machine translation becoming increasingly intelligent and powerful, our translation training maybe should pay more attention to the aspect of writing style, so that we can better cooperate with MT to produce effective output.
Student-centered Education in Metaverse: Transforming the Language Listening Curriculum

The paper explores transforming the English Listening curriculum for university freshmen with the aid of technology. The extension of Computer-assisted Language Learning (CALL) courses goes beyond the aspect of soft-ware and hardware, to the concept of the metaverse. Surveys have been conducted to collect students’ requirements and responses to probe the extent of their technology acceptance as well as expectations for the course. The surveyed 150 university freshmen are involved as research objects. The result based on the SPSS software analysis reveals that students’ language proficiency is not necessarily related to the fulfillment of instructors’ requirements. Despite the integration of multimedia technology for language learning in the class, university students usually have their own learning techniques. Teachers need to respect their methods and provide guidance based on their nature. Data retrieved from frequently used online learning platforms is analyzed to find out that freshmen students are highly dependent on offline interaction with the instructor. The paper concludes that teachers’ involvement in terms of on-site content delivery is a fundamental source of students’ motivation. Authors are also seeking a way of combing the advantages of both traditional classrooms and modern technology. Mataverse may be an appropriate setting to accommodate students’ proactive participation and language acquisition.