Reviewerly/exHarmony huggingface dataset:
A Noise-Removal of Knowledge Graph Framework for Profile-Based Spoken Language Understanding

To alleviate the ambiguity of user utterances, a profile-based spoken language understanding (PROSLU) task has been proposed recently, which uses supporting profile information as supplementary knowledge. The knowledge graph, as one of the supporting profile information, contains many entities with rich attribute information which do help the performance of SLU. However, existing models treat all entities as useful, resulting in a lot of noise being fed. Moreover, not all the attribute information of the entity is necessary, and there is a lot of noise and redundancy. In this paper, we propose a Noise-Removal of Knowledge Graph framework for PROSLU (NRKG-PROSLU), with two different kinds of denoising. One is a hard denoising by entity selection, where we introduce a small clean dataset and propose an auxiliary model, BERT-based entity selection, to filter out entities irrelevant to user utterances, namely noisy entities. The other is a soft denoising by entity attribute information selection, where we introduce keywords-based local semantic selection, which uses keywords to give more weight to relevant local semantics, so as to capture task-related information in the selected entities for reducing noise and redundancy. Then, the denoised knowledge graph is used to assist the SLU model, which achieves better performance than competitive models on the public PROSLU dataset.
Accurate, Diverse and Multiple Distractor Generation with Mixture of Experts

Given the background passage, question and answer, Distractor Generation (DG) aims to generate several incorrect options to confuse readers, which is an essential composition to build multiple choice question data. Most of the existing works apply naive methods to obtain multiple outputs with the sacrifice of the generation quality. In this paper, we propose an end-to-end one-to-many generation structure with mixture of experts (MoE) for DG, and explore how different data-to-expert routing strategies of MoE influence the performance of one-to-many generation models. Concretely, the model’s encoder calculates token-level attention vectors to mark important tokens from the source sequence, and the decoder generates multiple results with the guidance of the local attention. Moreover, we propose a minimal loss assignment mechanism and a stable routing strategy for diversity generation. Experimental results demonstrate that our proposed method is able to generate multiple distractors with good interpretability, which greatly outperforms the existing state-of-the-art DG models in quality, and achieves satisfactory lexical and semantic diversity.
Large Language Models are Diverse Role-Players for Summarization Evaluation

Text summarization has a wide range of applications in many scenarios. The evaluation of the quality of the generated text is a complex problem. A big challenge to language evaluation is that there is a clear divergence between existing metrics and human evaluation. A document summary's quality can be assessed by human annotators on various criteria, both objective ones like grammar and correctness, and subjective ones like informativeness, succinctness, and appeal. Most of the automatic evaluation methods like BLUE/ROUGE may be not able to adequately capture the above dimensions. In this paper, we propose a new evaluation framework based on LLMs, which provides a comprehensive evaluation framework by comparing generated text and reference text from both objective and subjective aspects. First, we propose to model objective and subjective dimensions of generated text based on roleplayers prompting mechanism. Furthermore, we introduce a context-based prompting mechanism that is able to generate dynamic roleplayer profiles based on input context. Finally, we design a multi-roleplayer prompting technology based on batch prompting and integrate multiple outputs into the final evaluation results. Experimental results on three real datasets for summarization show that our model is highly competitive and has a very high consistency with human annotators.
Imitation Attacks Can Steal More Than You Think from Machine Translation Systems

Attackers can easily steal the capabilities of a machine translation (MT) system by imitation attack without too much cost. However, few works pay attention to this topic. In this paper, we explore when and why the MT model can be stolen. We first empirically analyze imitation attacks and model stealing on MT tasks, finding that imitation attacks can steal the victim model from noisy query data, noisy models, and noisy translations, which are the typical methods for model defense. What’s more, the performance of the imitation model may even exceed the victim. By defining a KL distance of different corpora and using it to measure the similarity between the original data and stolen translations, we show that the imitation model steals MT systems relying on indirectly learning the distribution of the original data.
Time Series Momentum Trading Strategy for Cryptocurrencies

Contemporarily, cryptocurrencies have fascinated massive speculators and financial institutions owing to its high volatility and low-correlation with conditional underlying assets. Concurrently, the trading strategy for cryptocurrencies has always been a hot topic of research. This research investigates the feasibility and profitability of daily time-series momentum trading strategy for top 5 cryptocurrencies by market capitalization. We utilize a pluralism linear regression model that predicts returns based on historical data and construct optimal investments by momentum strategy and mean-variance model. According to the analysis, daily momentum strategy over short lookback horizons could generate significant positive profits and the robustness is checked by testing out of sample. The comparison to passive trading reveals that momentum trading with higher Sharpe ratios outperforms passive trading over mostly lookback periods. Furthermore, momentum trading could mostly yield markedly cumulative returns over a test period from January 2021 to August 2022 even in the presence of high drawdowns. As taking 0.1% transaction cost into account, the strategy in this paper could also achieve considerably returns. These results contribute to the further study on daily-frequency momentum strategy for cryptocurrencies and fill the gap in considering the impact of transaction costs on implementation of the strategy.
Auxiliary Information Enhanced Span-Based Model for Nested Named Entity Recognition

Span-based methods have unique advantages for solving nested named entity recognition (NER) problems. As primary information, boundaries play a crucial role in span representation. However, auxiliary information, which assists in identifying entities, still needs to be adequately investigated. In this work, We propose a simple yet effective method to enhance classification performance using boundaries and auxiliary information. Our model mainly consists of an adaptive convolution layer, an information-aware layer, and an information-agnostic layer. Adaptive convolution layers dynamically acquire words at different distances to enhance position-aware head and tail representations of spans. Information-aware and information-agnostic layers selectively incorporate boundaries and auxiliary information into the span representation and maintain boundary-oriented. Experiments show that our method outperforms the previous span-based methods and achieves state-of-the-art $$F_{1}$$ scores on four NER datasets named ACE2005, ACE2004, Weibo and Resume. Experiments also show comparable results on GENIA and CoNLL2003.
Evaluation Framework for Poisoning Attacks on Knowledge Graph Embeddings

In the area of knowledge graph embedding data poisoning, attackers are beginning to consider the importance of poisoning sample exposure risk while increasing the toxicity of the poisoning sample. On the other hand, we have found that some researchers incorrectly assess the effectiveness of poisoning attacks without considering the impact of the data-adding operation on model performance. Also, there is currently no definition of the Stealthiness of poisoning attacks. To address this issue, we provide an objective and unified framework for evaluating complex and diverse poisoning strategies. We design a controlled experiment on poisoning attacks to obtain objectively correct poisoning effects, and propose toxicity Dt to evaluate the poisoning performance of poisoning attacks and stealthiness Ds to evaluate the exposure risk of poisoning attacks. In designing the metrics, we fully considered the performance of the control model and the generalizability of the attacked model so that the data poisoning effect can be objectively and correctly evaluated. We compared 12 recently proposed KG attack methods on two different benchmark datasets to verify the objectivity and correctness of our evaluation criteria and to analyze the impact of the generalizability of the attacked model.
CrossDial: An Entertaining Dialogue Dataset of Chinese Crosstalk

Crosstalk is a traditional Chinese theatrical performance art. It is commonly performed by two performers in the form of a dialogue. With the typical features of dialogues, crosstalks are also designed to be hilarious for the purpose of amusing the audience. In this study, we introduce CrossDial, an open-source dataset containing most classic Chinese crosstalks crawled from the Web. Moreover, we define two new tasks, provide two benchmarks, and investigate the ability of current dialogue generation models in the field of crosstalk generation. The experiment results and case studies demonstrate that crosstalk generation is challenging for straightforward methods and remains an interesting topic for future works.
A Joint Entity and Relation Extraction Approach Using Dilated Convolution and Context Fusion

In recent years, researchers have shown increasing interest in joint entity and relation extraction. However, existing approaches overlook the interaction between words at different distances and the significance of contextual information between entities. We believe that the correlation strength of word pairs should be considered, and it is necessary to integrate contextual information into entities to learn better entity-level representations. In this paper, we treat named entity recognition as a multi-class classification of word pairs. We employ self-attention mechanism and design both local and multi-grained dilated convolution layers to capture spatial correlations between words. In the relation extraction module, we leverage attention from the self-attention layer to fuse localized context information into entity-pair to produce context-enhanced entity-level representations. In addition, we integrate named entity recognition and relation extraction through a multi-task learning framework, effectively leveraging the interaction between two subtasks. To validate the performance of our model, we conducted extensive experiments on joint entity and relation extraction benchmark datasets CoNLL04, ADE and SciERC. The experimental results indicate that our proposed model can achieve significant improvements over existing methods on these datasets.
Traffic‐aware rate control for mix‐flow in datacenter

Abstract Datacenter applications generate diverse flows, including deadline flows and non‐deadline flows. The deadline flows require to complete within strict deadline, while non‐deadline flows seek a shorter flow completion time. The state‐of‐the‐art deadline‐aware methods either transmit deadline flows with best‐effort at high priority, resulting in the starvation of non‐deadline flows, or blindly restrict the sending rates of deadline flows, leading to a high deadline missing ratio. To meet the different requirements of mix‐flows, a novel traffic‐aware rate control (TRC) method is proposed. TRC dynamically adjusts the sending rates of deadline flows according to their deadlines and the predicted future traffic patterns. If the intense competition is predicted among deadline flows, TRC will adopt a more aggressive manner to transmit the current deadline flows to avoid bandwidth contention in the future, reducing the deadline missing ratio. Otherwise, TRC will conservatively transmit deadline flows and complete these flows near their respective deadlines, relinquishing the excess bandwidth to non‐deadline flows. Meanwhile, TRC schedules non‐deadline flows in accordance with their sizes, minimizing the average FCT. The performance of TRC in large‐scale scenarios is evaluated through NS2 simulations. The test results show that TRC reduces the deadline missing ratio of deadline flows and the FCT of non‐deadline flows by up to 69.5% and 78.7% compared to the state‐of‐the‐art deadline‐aware schemes, respectively.
Evicting and filling attack for linking multiple network addresses of Bitcoin nodes

Abstract Bitcoin is a decentralized P2P cryptocurrency. It supports users to use pseudonyms instead of network addresses to send and receive transactions at the data layer, hiding users’ real network identities. Traditional transaction tracing attack cuts through the network layer to directly associate each transaction with the network address that issued it, thus revealing the sender’s network identity. But this attack can be mitigated by Bitcoin’s network layer privacy protections. Since Bitcoin protects the unlinkability of Bitcoin addresses and there may be a many-to-one relationship between addresses and nodes, transactions sent from the same node via different addresses are seen as coming from different nodes because attackers can only use addresses as node identifiers. In this paper, we proposed the evicting and filling attack to expose the correlations between addresses and cluster transactions sent from different addresses of the same node. The attack exploited the unisolation of Bitcoin’s incoming connection processing mechanism. In particular, an attacker can utilize the shared connection pool and deterministic connection eviction strategy to infer the correlation between incoming and evicting connections, as well as the correlation between releasing and filling connections. Based on inferred results, different addresses of the same node with these connections can be linked together, whether they are of the same or different network types. We designed a multi-step attack procedure, and set reasonable attack parameters through analyzing the factors that affect the attack efficiency and accuracy. We mounted this attack on both our self-run nodes and multi-address nodes in real Bitcoin network, achieving an average accuracy of 96.9% and 82%, respectively. Furthermore, we found that the attack is also applicable to Zcash, Litecoin, Dogecoin, Bitcoin Cash, and Dash. We analyzed the cost of network-wide attacks, the application scenario, and proposed countermeasures of this attack.
Edge assignment in edge federated learning

Abstract Federated Learning (FL) is a recent Machine Learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Because FL depends on a central server for repeated aggregation of local training models, this server is prone to become a performance bottleneck. Therefore, one can combine FL with Edge Computing: introduce a layer of edge servers to each serve as a regional aggregator to offload the main server. The scalability is thus improved, however at the cost of learning accuracy. We show that this cost can be alleviated with a proper choice of edge server assignment: which edge servers should aggregate the training models from which local machines. In this paper, we propose an assignment solution for this purpose. Our solution is especially useful for the case of non-IID training data which is well-known to hinder today’s FL performance. Our findings are substantiated with an evaluation study using real-world datasets.
Efficient Auto‐scaling for Host Load Prediction through VM migration in Cloud

Summary The expeditious deployment of Cloud applications and services on wide‐ranging Cloud Data Centres (CDC) gives rise to the utilization of many resources. Moreover, by the increase in resource utilization, virtualization also greatly impacts achieving desired performance. The major challenges in virtualization are detecting over‐utilized or under‐utilized hosts at the right time and the proper scaling of Virtual Machines (VM) on the accurate host. Auto‐scaling in Cloud Computing allows the service providers to scale up or down the resources automatically and provides on‐demand computing power and storage capacities. Effective utilization and autonomous scaling of resources eventually reduce the load, energy consumption, and operating costs. In this paper, an efficient auto‐scaling approach for predicting host load through VM migration has been proposed. The ensemble method using different time‐series forecasting models has been proposed to forecast the approaching workload on the host. Based on this predicted load, different algorithms have been devised to detect over‐utilized and under‐utilized hosts and VMs can be migrated. The designed approach has been validated by experimentation on a real‐time Google cluster dataset. The proposed technique significantly improves average CPU utilization and reduces over‐utilization and under‐utilization. It also minimizes response time, service level agreement violations, and the slighter number of migrations and scaling overhead.
Blockchain-based Electronic Health Record Management System

The Blockchain-based Electronic Health Record (B-EHR) system represents a significant advancement in healthcare data management. Concerns over data confidentiality and security have become increasingly critical in the healthcare sector, given the need for immediate data accessibility. Traditional centralized systems face accessibility issues, necessitating a transformative solution, and blockchain technology emerges as a promising candidate. This research introduces a patient-controlled, blockchain-based system that efficiently manages and safeguards individuals' health-related data. By harnessing the Ethereum network and utilizing tools such as Ganache, Solidity, and web3.js, this system takes a systematic approach to overcome the limitations of centralized systems. Smart contracts, the basis of blockchain technology, serve as the backbone for storing and processing patients' data in a decentralized manner. Transactions are conducted securely through these smart contracts, ensuring patient privacy and data security. Notably, any modifications to transactions can be verified and propagated across the entire distributed network, enhancing data integrity. Complementing this system is a cryptocurrency wallet like MetaMask, providing a centrally controlled repository where records can be swiftly accessed and secured by authorized individuals, including doctors and patients. This integration significantly improves data accessibility and security within the healthcare domain. Ultimately, this research aims to leverage blockchain technology for simultaneous data retrieval, enhancing efficiency, credibility, and accessibility. It offers a robust framework for securely storing data with tailored access permissions and facilitates the safe transfer of patient medical records. In essence, it introduces a swift and secure health record system and an innovative protocol, promoting greater transparency and ownership of sensitive data in the healthcare sector through blockchain integration.
White Paper on Cloud Computing: Overcoming Challenges of Integrating Robust Competition with Security

Submission to Federal Trade Commission, Rulemaking Docket - “Request for Information-Solicitation for Public Comments on the Business Practices of Cloud Computing Providers,” Comment #FTC-2023-0028-0023, Track.No. lhz-0pzs-bo1wp (May 21, 2023) and Lecture Presentation, Academy of Legal Studies in Business, San Diego CA (July 29, 2923).Cloud computing is a growing service industry supplying an expanding portfolio of individual services rendered through intermediate service providers and directly to end-user clients. Such services are roughly classified as remote data storage, user-access interface and data processing. Regulators around the world are scrutinizing the contracting practices, industrial organization (I/O) and the resulting quality of service levels in cloud computing service markets. This white paper analyzes the cloud’s architecture - it is the essential underpinning to regulatory success in encouraging security investment, standardization, information governance, industrial policies providing economic incentives and the cloud’s achievement of critical infrastructure importance.
Which Came First, Price or Activity? A Vicious Circle of a Blockchain-based Social Media in the Bear Market

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A Study and Implementation of Load Balancing Services provided by Microsoft Azure Cloud Service Provider

Load balancing refers to evenly distributing load (incoming network traffic) across a group of backend resources or servers. Azure Load Balancer operates at layer 4 of the Open Systems Interconnection (OSI) model. It's the single point of contact for clients. Load balancer distributes inbound flows that arrive at the load balancer's front end to backend pool instances. These flows are according to configured load-balancing rules and health probes. The backend pool instances can be Azure Virtual Machines or instances in a Virtual Machine Scale Set. With Azure Load Balancer, you can scale your applications and create highly available services. Load balancer supports both inbound and outbound scenarios. Load balancer provides low latency and high throughput, and scales up to millions of flows for all TCP and UDP applications.
More Than Meets the Eye: Analyzing Anesthesiologists’ Visual Attention in the Operating Room Using Deep Learning Models

Patient’s vital signs, which are displayed on monitors, make the anesthesiologist’s visual attention (VA) a key component in the safe management of patients under general anesthesia; moreover, the distribution of said VA and the ability to acquire specific cues throughout the anesthetic, may have a direct impact on patient’s outcome. Currently, most studies employ wearable eye-tracking technologies to analyze anesthesiologists’ visual patterns. Albeit being able to produce meticulous data, wearable devices are not a sustainable solution for large-scale or long-term use for data collection in the operating room (OR). Thus, by utilizing a novel eye-tracking method in the form of deep learning models that process monitor-mounted webcams, we collected continuous behavioral data and gained insight into the anesthesiologist’s VA distribution with minimal disturbance to their natural workflow. In this study, we collected OR video recordings using the proposed framework and compared different visual behavioral patterns. We distinguished between baseline VA distribution during uneventful periods to patterns associated with active phases or during critical, unanticipated incidents. In the future, such a platform may serve as a crucial component of context-aware assistive technologies in the OR.