Reviewerly/exHarmony huggingface dataset:
Web Page Segmentation: A DOM-Structural Cohesion Analysis Approach

Web page segmentation is a fundamental technique applied in information retrieval systems to enhance web crawling tasks and information extraction. Its objectives are to gain deep insights from crawling results and to extract the main content of a webpage by disregarding the irrelevant regions. Over time, several solutions have been proposed to address the segmentation problem using different approaches and learning strategies. Among these, the structural cue, which is a characteristic of the DOM tree, is widely utilized as a primary factor in segmentation models. In this paper, we propose a novel technique for web page segmentation using DOM-structural cohesion analysis. Our approach involves generating blocks that represent groups of DOM subtrees with similar tag structures. By analyzing the cohesion within each generated block and comparing detailed information such as types, attributes, and visual cues of web page elements, the approach can effectively maintain or reconstruct the segmentation layout. Additionally, we employ the Canny algorithm to optimize the segmentation result by reducing redundant spaces, resulting in a more accurate segmentation. We evaluate the effectiveness of our approach using a dataset of 1,969 web pages. The approach achieves 64% on the $$\displaystyle F_{B^{3}}$$ score, surpassing existing state-of-the-art methods. The proposed DOM-structural cohesion analysis has the potential to improve web page segmentation and its various applications.
Towards Human-Centric Endpoint Security

In a survey of six widely used end-to-end encrypted messaging applications, we consider the post-compromise recovery process from the perspective of what security audit functions, if any, are in place to detect and recover from attacks. Our investigation reveals audit functions vary in the extent to which they rely on the end user. We argue developers should minimize dependence on users and view them as a residual, not primary, risk mitigation strategy. To provide robust communications security, E2EE applications need to avoid protocol designs that dump too much responsibility on naive users and instead make system components play an appropriate role.
Informative Anchor-Enhanced Heterogeneous Global Graph Neural Networks for Personalized Session-Based Recommendation

Due to the anonymity of user sessions, most existing session-based recommender systems (SBRSs) cannot effectively learn user features, leading to failure to make personalized recommendations. Besides, these SBRSs may neglect some similar items with common features if they are long-distance in the session graphs or global graphs. In this paper, we propose a novel SBRS based on heterogeneous graph neural network, which can effectively learn user and item embeddings for personalized recommendations. Furthermore, we find out the user and item informative anchors in the heterogeneous graph and propagate their features in the same type of nodes, which can help to explore those long-distance but similar items. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate the effectiveness of our proposed method.
Local Difference-Based Federated Learning Against Preference Profiling Attacks

The recommendation system based on federated learning has become one of the most popular distributed machine learning technologies, which to some extent protects the privacy and security of users. However, personal privacy information can still be disclosed through privacy inference attacks. In order to resist preference profiling attack (PPA), we propose a differential privacy federated learning recommendation method based on federated neural collaborative filtering. We construct a neural collaborative filtering recommendation method based on federated learning with local differential privacy, which can achieve the effect similar to that of federal neural collaborative filtering recommendation. First, we compared three instances of neural collaborative filtering, i.e., Multi-Layer Perceptron (MLP), Generalized Matrix Factorization (GMF), and Neural Matrix Factorization (NeuMF) combined with federated learning, under the same conditions, in order to select the optimal instance. And then the training parameters varied using Gaussian noise of the gradient of the uninteracted items and Laplace noise of the interacted items. Under the same evaluation indicators, the training loss rate has decreased by 1.61%, and the recommendation hit rate hit_ratio@10 was improved by 3.05%, normalize discounted cumulative gain ndcg@10 was reduced by 2.55%. The experimental results validated the feasibility of this method, indicating that the improvement of the algorithm is effective.
Fraudulent Jobs Prediction Using Natural Language Processing and Deep Learning Sequential Models

The increase of job postings being made available, particularly online, has led to an increase in fraudulent job postings, designed to trick applicants into offering up their money and personal information. Previous works that proposed solutions to this issue, typically avoid analyzing sentiment to do this and instead rely only on information that can be converted into a categorical form. This paper proposes a methodology using deep learning sequential models in conjunction with natural language processing techniques to solve the fraudulent job posting issues. We test several text pre-processing techniques and word vectorization models, and experiments are performed to identify which combination of these techniques/vectorization models achieve the best results on the chosen deep learning models. The dataset used suffers from an imbalance issue, thus several different over-sampling techniques as well as the use of class weightings were tested to mitigate this. Through this experimentation, we were able to achieve a best accuracy of 98%, with a precision of 93%, recall of 89% and an F1 Score of 91%, utilizing data balanced using class weightings, a pre-trained Word2Vec embedding model and a Bi-Directional LSTM as our deep learning sequential model.
Blind Auditing and Probabilistic Access Controls

Keeping audit logs about past activities in any computer system is one of the requirements regulated decades ago. However, there is no clear definition of what information should be kept in audit logs. Therefore, most existing systems include identifiers in audit records that violate data privacy, or they apply pseudonymization techniques before logging that add unnecessary overhead to the system. In this paper, we define an auditing approach called blind auditing in which the audit logs contain just “enough” information to preserve data privacy and enable different auditing processes without adding intolerable overhead to the system being audited. With those blind audit logs, we define a probabilistic access control model that dynamically improves over time. Together, these allow auditing and access control that is performant, privacy-preserving and perpetually improving.
Determining an Economic Value of High Assurance for Commodity Software Security (Transcript of Discussion)

This paper is on how to determine an economic value of high assurance for commodity software security.
Scaling Machine Learning with an Efficient Hybrid Distributed Framework

Hybrid distributed frameworks combine the advantage of data parallelism and model parallelism, thus achieving better model performance and training efficiency. However, the existing hybrid architectures still suffer from low efficiency caused by the high communication cost and global barriers problem. In this paper, we propose a more efficient hybrid distributed framework that trains sub-models asynchronously and predicts in an ensemble learning way. To divide a global model into multiple independent sub-models, we propose a general model partition approach which constructs the unique sub-model by aggregating feature sets of training data into multiple independent feature subsets. To further improve the performance of our framework, we design a pruning strategy that stops the poor sub-model early and assigns the idle computation resource to active model training groups. The experimental results show that the proposed algorithm achieves significantly better performance (1.82%–4.58% RMSE) than the state-of-the-art baselines and also achieves a 3.7 $$\times $$ –7.9 $$\times $$ speedup when reaching a comparable RMSE performance.
CREAM: Named Entity Recognition with Concise query and REgion-Aware Minimization

Recent advancements in Machine Reading Comprehension (MRC) models have sparked interest in the field of Named Entity Recognition (NER), where entities are extracted as answers of given queries. Yet, existing MRC-based models face several challenges, including high computational costs, limited consideration of entity content information, and the tendency to generate sharp boundaries, that hinder their generalizability. To alleviate these issues, this paper introduces CREAM, an enhanced model leveraging Concise query and REgion-Aware Minimization. First, we propose a simple yet effective strategy of generating concise queries based primarily on entity categories. Second, we propose to go beyond existing methods by identifying entire entities, instead of just their boundaries (start and end positions), with an efficient continuous cross-entropy loss. An in-depth analysis is further provided to reveal their benefit. The proposed method is evaluated on six well-known NER benchmarks. Experimental results demonstrate its remarkable effectiveness by surpassing the current state-of-the-art models, with the substantial averaged improvement of 2.74, 1.12, and 2.38 absolute percentage points in Precision, Recall, and F1 metrics, respectively.
Multi-level Correlation Matching for Legal Text Similarity Modeling with Multiple Examples

Legal artificial intelligence (LegalAI) is an emerging field that leverages AI technology to enhance legal services. Similar Case Matching (SCM), which calculates the relevance between a candidate and a target case, is a critical technique in LegalAI to enable diverse legal intelligences. Existing approaches mainly rely the on single query texts or specific keywords for retrieval, yet neglected the domain complexity and multi-faceted nature of queries. Thus, a multi-example matching paradigm is motivated where three inherent challenges reveal. 1) Relevance assessment across multiple examples is complex. 2) The inherent lengthy and structured property of legal documents. 3) Lacking datasets containing golden labels for multi-example-based legal text matching. To address these challenges, this paper develops a novel multi-example dataset, and a Multi-level Correlation Semantic Matching (MCSM) is devised to extract similarity between cases given multi-example inputs. The proposed multi-level scheme can be interpreted as two aspects. Firstly, we consider both content and structure correlations to evaluate the relevance. Secondly, by dividing legal documents into distinctive segments, we can hierarchically learn the intra- and inter-segment dependencies to model the long-term dependencies across components of legal documents. An attention mechanism is employed to capture the complex interconnections among these examples and enable an attentive matching aggregation of content and structure. With multiple examples, the MCSM tackles the intricate and diverse nature of legal queries, providing a comprehensive and multi-dimensional description view. Extensive experimental evaluations show that the proposed MCSM outperforms baseline methods.
Authentication of IT Professionals in the Wild – A Survey

The role of user authentication in software repositories can significantly impact those using open-source projects as a basis for their products. In addition to highlighting the importance of authentication in software supply chain security, we surveyed open-source developers to identify if these IT professionals take advantage of more secure authentication methods in open-source projects to mitigate common risks. We present results from a survey of 83 employees of an open-source software company. We found that these users mostly use two-factor authentication and perceive username and password as the most usable method. Regarding security, hardware and software tokens were perceived as the most secure methods. Using a third-party service for fallback authentication emerged as a non-preferred solution.
Trusted Introductions for Secure Messaging

Although today’s most prevalent end-to-end encrypted messaging platforms using the Signal Protocol perform opportunistic encryption and provide resistance to eavesdropping, they are still vulnerable to impersonation attacks. We propose Trusted Introductions, a mechanism to transfer existing identity verifications between users, to increase resistance to active attacks. The proposal builds on the out-of-band user identity verification capabilities provided by the Signal Protocol. We argue that replacing user-managed identity-keys in cryptographic systems with the concept of an introduction, will increase users’ understanding and improve usability of the verification mechanism. Current events underscore the need for anonymous introductions, which can be achieved based on the Signal Protocol’s properties of forward secrecy and repudiation.
Knowledge-Grounded Dialogue Generation with Contrastive Knowledge Selection

Knowledge selection is the key component in knowledge-ground dialogues, which aims to choice correct knowledge based on external knowledge for dialogue generation. The quality of knowledge selection depend on knowledge representation methods. However, the knowledge representation exploration is still challenging. We propose a knowledge-grounded dialogue model, which incorporates a knowledge-grounded module and a dialogue generation module, aiming to choose the most appropriate knowledge and fuse it into response generation. In addition, supervised contrastive knowledge representation signal is designed to obtain knowledge representation dynamically. Experiments on FoCus dataset show that our model outperforms the baseline models. The ablation study further demonstrates the effectiveness of each sub-modules.
A Dual-Layer Privacy-Preserving Federated Learning Framework

With the exponential growth of personal data use for machine learning models, significant privacy challenges arise. Anonymisation and federated learning can protect privacy-sensitive data at the cost of accuracy but there is lack of research on hybrid approaches. This paper uses federated learning and traditional centralised machine learning to evaluate the effectiveness of different anonymization strategies in environments with independent and identically distributed data. It considers the two layers of data collection (layer one) and model training (layer two) on three scenarios: (i) local data collection and local anonymisation for federated model training, (ii) central data collection before anonymisation for centralised model training, and (iii) central aggregation of locally anonymised data for centralised model training. Our assessment shows that the performance of the models generally decreases with increasing anonymity constraints, but the extent of decrease varies across different scenarios. In addition, we propose a dual-layer federated learning framework that applies differential privacy to ensure privacy during both data collection and model training stages. Evaluation on real-world datasets demonstrates that our framework achieves both acceptable data anonymization and model accuracy.
Incentives and Censorship Resistance for Mixnets Revisited

Our position is that incentives are fundamental to anonymous communications and censorship resistance; they have been largely forgotten in practice despite being investigated on many occasions over the years. A history of the need for incentives in anonymous communication networks is given, and discussed in the context of deployed networks. We consider privacy from an economic perspective of supply and demand: First, there has to be servers that supply computational power needed to privacy-enhance traffic in return for rewards; second, there needs to be paying users to create the demand to provision those rewards. We present twin hypotheses that run counter to much of the current research in anonymous communication networks such as Tor as well as censorship resistance: the use of a tokenized incentives will create a population of servers to supply anonymity while users in countries such as China can adapt protocols where decentralization powers the demand for uncensored network access. Finally, we sketch how the Nym mixnet is exploring these hypotheses.
Blockchain-Empowered Resource Allocation and Data Security for Efficient Vehicular Edge Computing

Vehicular networking technology is advancing rapidly, and one promising area of research is blockchain-based vehicular edge computing to enhance resource allocation and data security. This paper aims to optimize resource allocation and data security in vehicular edge computing by leveraging blockchain technology, thereby improving the overall system efficiency and reliability. By integrating the computational, storage, and communication capabilities of vehicles with blockchain, efficient utilization of edge computing and fair resource allocation are achieved. Additionally, data encryption techniques are introduced to ensure data security and privacy protection. Experimental results demonstrate that the system can automatically identify and allocate the most suitable edge computing nodes, thereby enhancing the responsiveness and quality of computational tasks. The blockchain-based vehicular edge offloading system not only optimizes the performance of vehicular networking systems and enhances user experience but also strengthens data security and privacy protection, providing a novel solution for the development of the vehicular networking industry.
Noise-Augmented Contrastive Learning for Sequential Recommendation

Recently, contrastive learning has been widely used in the field of sequential recommendation to solve the data sparsity problem. CL4Rec augments data through simple random crop, mask, and reorder, while DuoRec proposes a model-level data augmentation method. However, these methods do not take into account the issue of noisy data in sequential recommendation, such as false clicks during browsing. The noise may lead to poor representations of learned sequences and negatively affect the augmented data. Current sequential recommendation methods tend to learn the user’s intention from their original sequences, but these methods have certain limitations as the user’s intention for the next interaction may change. Based on the above observations, we propose Noise-augmented Contrastive Learning for Sequential Recommendation (NCL4Rec). Our NCL4Rec proposes sequential noise probability-guided data augmentation. We introduce supervised noise recognition during training instead of obtaining it from original sequences. Moreover, we design positive and negative augmentations of the sequence and design unique noise loss function to train them. Through experiments, it is verified that our NCL4Rec consistently outperforms the current state-of-the-art models.
Domain Adaptation with Sample Relation Reinforcement

Domain adaptation transfers knowledge from the source domain into the target domain by minimizing the distribution discrepancy between two domains. Recently, existing approaches ignore the position differences between samples within the same class. However, there are still two problems that need to be resolved. Firstly, marginal samples and central samples may interact with each other, which leads to samples within the same class aggregate toward the margin (loss common knowledge) or toward the center (loss specific knowledge). Secondly, large distribution differences may result in degraded performance in classification. To solve these two problems, we propose a domain adaptation method called Sample Relation Reinforcement (SRR). To mitigate the interaction between marginal samples and central samples, marginal samples detection (MSD) and central samples detection (CSD) are proposed. Specifically, MSD propose a triple distances metric among the sample pairs and class centers to detect the marginal samples and learn specific knowledge. CSD utilizes the nearest and second-nearest centroid of samples to detect the central samples and learn common knowledge. In addition, dual centroid detection (DCD) is proposed to alleviate the negative effects of distribution differences, which utilizes the class centers of the source and target domains to classify the target samples. Finally, the experimental results on four datasets demonstrate that the proposed method outperforms other competing approaches significantly.
An Adaptive Drilling Sampling Method and Evaluation Model for Large-Scale Streaming Data

The sampling methods for real-time and high-speed changing streaming data are prone to lose a large amount of valuable discrete data. The SDDS (Streaming Data Dynamic Drilling Sampling) sampling method can obtain the value of a large amount of discrete data, but it retains too many discrete values in the sample set, resulting in inconsistent distribution with the original stream dataset and difficulty in describing the overall characteristics of the streaming data. Based on the SDDS sampling method, we propose an adaptive drilling sampling method SDADS (Streaming Data Adaptive Drilling Sampling), which adaptively adjusts various sampling rates in the well during the sampling process, caches all data in the current well, and then adaptively resamples the data in the well, ensuring consistency in data distribution before and after sampling. A new model SDVM (Streaming Data Valuation Model), is further proposed for evaluating the overall value characteristics of streaming datasets. Experiments show that the method proposed in the paper uses neural network training and testing with a small sampling rate to achieve accuracy, recall, and F1 scores above 90%, which is higher than that of the SDDS sampling method. In summary, the SDADS sampling method is beneficial to train neural network models and evaluate the overall value characteristics of streaming data, which has essential research significance in big data valuation.
Fuzzy Based Text Quality Assessment for Sentiment Analysis

Practitioners have emphasized the importance of employing sentiment analysis techniques in decision-making. The data utilized in this process is typically gathered from social media, making it somewhat unreliable for decision-making. To address this issue, this study focuses on the Text Quality (TQ) aspect to capture the characteristics of Twitter data streams. Our objective is to develop an automated approach that assists the user in assessing the quality of textual data. This is accomplished through a fuzzified classifier, which automatically identifies ambiguous and unambiguous text at both the syntactic and semantic levels. We present a software tool that captures real-time and batch Twitter data streams. This tool calculates their TQ and presents the outcomes through diverse graphical depictions. It also empowers users to customize the weights allocated to individual quality dimensions and metrics used in computing the overall data quality of a tweet. This flexibility enables customization of weights according to different analysis contexts and user profiles. To demonstrate the usability and value of our contributions, we conducted a case study focusing on the Covid-19 vaccine. A preliminary analysis shows that by removing ambiguous text, the accuracy of the deployed algorithms enhances.